v2: Full product redesign

- 6-module tab layout (Agent Chat, Target, Drug, Disease, Company, Trial)
- Natural language agent with intent parsing & tool routing
- Product-grade CSS (cards, badges, animations, custom theme)
- MCP live data integration with knowledge base fallback
- Structured report generation for each module
- Quick examples & featured reports
- README with HF Space metadata

.gitignore

@@ -0,0 +1,4 @@
+__pycache__/
+.venv/
+*.pyc
+.env

README.md

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+---
+title: Pharma Intelligence
+emoji: 🧬
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.0.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# 🧬 PatSnap Pharma Intelligence
+
+**AI-Powered Drug Discovery Intelligence Agent** — explore targets, drugs, diseases, companies, and clinical trials.
+
+## Modules
+
+| Module | Description |
+|--------|-------------|
+| 🤖 **Agent Chat** | Natural language → intent routing → structured report |
+| 🎯 **Target Intelligence** | Deep analysis of drug targets (biology, drugs, pipeline) |
+| 💊 **Drug Exploration** | Pipeline drugs by target, disease, mechanism, or company |
+| 🏥 **Disease Investigation** | Disease landscape, epidemiology, treatments |
+| 🏢 **Company Profiling** | Pharma pipeline & strategic analysis |
+| 🧪 **Clinical Trials** | Trial landscape by indication, phase, sponsor |
+
+## How It Works
+
+1. **Ask in natural language** — The agent parses your intent
+2. **MCP-powered search** — Connects to PatSnap's pharmaceutical intelligence database
+3. **Structured reports** — Generates professional-grade reports with pipeline tables, statistics, and insights
+
+## Configuration
+
+### API Key (Optional)
+
+Set the `PATSNAP_API_KEY` environment variable in your Space settings to enable live pharmaceutical data. Without it, the demo uses a built-in knowledge base.
+
+```bash
+PATSNAP_API_KEY=your_key_here
+```
+
+## Powered By
+
+- [PatSnap Life Sciences MCP](https://github.com/patsnap/skills)
+- [Gradio](https://gradio.app)

app.py

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+#!/usr/bin/env python3
+"""
+PatSnap Pharma Intelligence — Hugging Face Space Demo
+Product-grade multi-module AI agent for life science intelligence.
+
+Modules:
+  - Agent Chat (natural language → tool orchestration → report)
+  - Target Intelligence    (靶点全景)
+  - Drug Exploration       (药物管线)
+  - Disease Investigation  (疾病格局)
+  - Company Profiling      (公司分析)
+  - Clinical Trials        (临床试验)
+"""
+
+import os, json, asyncio, time, re
+from collections import Counter
+from datetime import datetime
+from typing import Optional, Dict, List, Tuple
+
+import gradio as gr
+
+# =============================================================================
+# CONFIGURATION
+# =============================================================================
+
+API_KEY = os.getenv("PATSNAP_API_KEY", "")
+HF_TOKEN = os.getenv("HF_TOKEN", "")  # optional: enable LLM agent mode
+SERVER_URL = f"https://connect.patsnap.com/096456/logic-mcp?apiKey={API_KEY}"
+
+# Module definitions
+MODULES = {
+    "target":  {"icon": "🎯", "label": "Target Intelligence", "label_cn": "靶点全景",
+                "tool": "ls_target_search", "entity": "target",
+                "desc": "Analyze any biomedical target — biology, drugs, pipeline, trials."},
+    "drug":    {"icon": "💊", "label": "Drug Exploration", "label_cn": "药物管线",
+                "tool": "ls_drug_search", "entity": "drug",
+                "desc": "Search drugs by target, disease, mechanism, or company."},
+    "disease": {"icon": "🏥", "label": "Disease Investigation", "label_cn": "疾病格局",
+                "tool": "ls_disease_search", "entity": "disease",
+                "desc": "Understand disease landscape — epidemiology, treatments, pipeline."},
+    "company": {"icon": "🏢", "label": "Company Profiling", "label_cn": "公司分析",
+                "tool": "ls_company_search", "entity": "company",
+                "desc": "Profile pharma companies — pipeline, deals, therapeutic focus."},
+    "trial":   {"icon": "🧪", "label": "Clinical Trials", "label_cn": "临床试验",
+                "tool": "ls_clinical_trial_search", "entity": "trial",
+                "desc": "Explore clinical trials by target, disease, phase, or sponsor."},
+}
+
+# Language: 'en' or 'zh'
+DEFAULT_LANG = "en"
+
+# =============================================================================
+# MOCK DATA — Comprehensive, module-specific
+# =============================================================================
+
+MOCK_TARGETS = {
+    "EGFR": {
+        "name": "EGFR", "full_name": "Epidermal Growth Factor Receptor",
+        "family": "ErbB/HER receptor tyrosine kinase",
+        "class": "Kinase", "indication_count": 12, "drug_count": 28,
+        "pdb_ids": ["1M17", "1XKK", "2J5F"],
+        "pathways": ["RAS/RAF/MEK/ERK", "PI3K/AKT/mTOR", "JAK/STAT"],
+        "mutation_hotspots": ["L858R (exon 21)", "exon 19 deletion", "T790M (exon 20)", "C797S (exon 20)"],
+        "approved_drugs": [
+            {"name": "Osimertinib", "type": "Small molecule", "gen": "3rd-gen TKI",
+             "year": 2015, "indications": ["NSCLC (T790M+)", "NSCLC (1L EGFRm)", "NSCLC (adjuvant)"],
+             "company": "AstraZeneca"},
+            {"name": "Gefitinib", "type": "Small molecule", "gen": "1st-gen TKI",
+             "year": 2003, "indications": ["NSCLC (EGFRm)"], "company": "AstraZeneca"},
+            {"name": "Erlotinib", "type": "Small molecule", "gen": "1st-gen TKI",
+             "year": 2004, "indications": ["NSCLC", "Pancreatic"], "company": "Roche/Genentech"},
+            {"name": "Afatinib", "type": "Small molecule", "gen": "2nd-gen TKI",
+             "year": 2013, "indications": ["NSCLC (EGFRm)"], "company": "Boehringer Ingelheim"},
+            {"name": "Dacomitinib", "type": "Small molecule", "gen": "2nd-gen TKI",
+             "year": 2018, "indications": ["NSCLC (EGFRm)"], "company": "Pfizer"},
+            {"name": "Cetuximab", "type": "Monoclonal antibody", "gen": "mAb",
+             "year": 2004, "indications": ["CRC", "HNSCC"], "company": "Merck KGaA / BMS"},
+            {"name": "Panitumumab", "type": "Monoclonal antibody", "gen": "mAb",
+             "year": 2006, "indications": ["CRC"], "company": "Amgen"},
+            {"name": "Amivantamab", "type": "Bispecific antibody", "gen": "BsAb",
+             "year": 2021, "indications": ["NSCLC (ex20ins)"], "company": "Janssen"},
+            {"name": "Patritumab deruxtecan", "type": "ADC", "gen": "ADC (HER3)",
+             "year": 2024, "indications": ["NSCLC (post-TKI)"], "company": "Daiichi Sankyo / Merck"},
+        ],
+        "pipeline_summary": {
+            "phase_3": 45, "phase_2": 120, "phase_1": 85, "preclinical": 200,
+            "hot_topics": ["4th-gen TKIs (C797S)", "Bispecific ADCs", "PROTAC degraders",
+                          "Combination with immunotherapy", "Brain-penetrant TKIs"],
+        },
+        "competitive_landscape": "Highly competitive — every major pharma has an EGFR asset. "
+                                 "Innovation is focused on resistance mechanisms and next-gen modalities.",
+    },
+    "HER2": {
+        "name": "HER2", "full_name": "Human Epidermal Growth Factor Receptor 2",
+        "family": "ErbB/HER receptor tyrosine kinase",
+        "class": "Kinase", "indication_count": 5, "drug_count": 15,
+        "pdb_ids": ["1N8Z", "3PP0"],
+        "pathways": ["RAS/RAF/MEK/ERK", "PI3K/AKT/mTOR"],
+        "mutation_hotspots": ["Amplification (breast/gastric)", "Exon 20 mutations (NSCLC)"],
+        "approved_drugs": [
+            {"name": "Trastuzumab", "type": "Monoclonal antibody", "gen": "mAb",
+             "year": 1998, "indications": ["HER2+ Breast Cancer", "HER2+ Gastric Cancer"], "company": "Roche"},
+            {"name": "Trastuzumab deruxtecan", "type": "ADC", "gen": "ADC",
+             "year": 2019, "indications": ["HER2+ Breast Cancer", "HER2-low Breast Cancer", "HER2+ Gastric Cancer",
+                                          "HER2-mutant NSCLC"], "company": "Daiichi Sankyo / AstraZeneca"},
+            {"name": "Pertuzumab", "type": "Monoclonal antibody", "gen": "mAb",
+             "year": 2012, "indications": ["HER2+ Breast Cancer"], "company": "Roche"},
+            {"name": "Lapatinib", "type": "Small molecule", "gen": "TKI",
+             "year": 2007, "indications": ["HER2+ Breast Cancer"], "company": "Novartis"},
+            {"name": "Tucatinib", "type": "Small molecule", "gen": "TKI",
+             "year": 2020, "indications": ["HER2+ Breast Cancer (CNS mets)"], "company": "Seagen / Merck"},
+        ],
+        "pipeline_summary": {
+            "phase_3": 25, "phase_2": 80, "phase_1": 55, "preclinical": 130,
+            "hot_topics": ["HER2-low targeting", "Bispecific ADCs", "Brain metastasis"],
+        },
+        "competitive_landscape": "HER2 ADC space is the current battleground. Enhertu dominates; "
+                                 "competitors focus on differentiation via payload, DAR, or epitope.",
+    },
+    "PD-L1": {
+        "name": "PD-L1", "full_name": "Programmed Death-Ligand 1",
+        "family": "B7 immune checkpoint",
+        "class": "Immune checkpoint ligand", "indication_count": 20, "drug_count": 20,
+        "approved_drugs": [
+            {"name": "Atezolizumab", "type": "Monoclonal antibody", "gen": "Anti-PD-L1 mAb",
+             "year": 2016, "indications": ["NSCLC", "SCLC", "Urothelial", "HCC"], "company": "Roche"},
+            {"name": "Durvalumab", "type": "Monoclonal antibody", "gen": "Anti-PD-L1 mAb",
+             "year": 2017, "indications": ["NSCLC (Stage III)", "SCLC", "Biliary Tract"], "company": "AstraZeneca"},
+            {"name": "Avelumab", "type": "Monoclonal antibody", "gen": "Anti-PD-L1 mAb",
+             "year": 2017, "indications": ["Merkel Cell", "Urothelial", "RCC"], "company": "Merck KGaA / Pfizer"},
+        ],
+        "pipeline_summary": {"phase_3": 35, "phase_2": 60, "phase_1": 40, "preclinical": 100},
+        "competitive_landscape": "PD-L1 is a companion to PD-1 — the focus is on combination strategies "
+                                 "and predictive biomarker development.",
+    },
+}
+
+MOCK_COMPANIES = {
+    "Roche": {
+        "name": "Roche", "ticker": "ROG.SW",
+        "headquarters": "Basel, Switzerland",
+        "employees": "~100,000",
+        "2024_revenue": "$65.4B",
+        "therapeutic_areas": ["Oncology", "Neuroscience", "Ophthalmology", "Immunology", "Infectious Disease"],
+        "flagship_drugs": [
+            {"name": "Trastuzumab (Herceptin)", "target": "HER2", "sales": "$3.2B", "phase": "Approved"},
+            {"name": "Atezolizumab (Tecentriq)", "target": "PD-L1", "sales": "$4.8B", "phase": "Approved"},
+            {"name": "Bevacizumab (Avastin)", "target": "VEGF", "sales": "$2.1B", "phase": "Approved"},
+            {"name": "Trastuzumab deruxtecan (co-developed)", "target": "HER2", "sales": "$3.5B", "phase": "Approved"},
+        ],
+        "pipeline_count": {"approved": 28, "phase_3": 15, "phase_2": 35, "phase_1": 22},
+        "recent_deals": [
+            "Acquired Carmot Therapeutics (obesity) — $2.7B upfront (2023)",
+            "Acquired Telavant (IBD) — $7.1B (2023)",
+        ],
+        "strategy": "Roche combines strong internal R&D with strategic bolt-on acquisitions. "
+                     "Oncology remains the core, with growing investment in immunology and metabolic disease.",
+    },
+    "AstraZeneca": {
+        "name": "AstraZeneca", "ticker": "AZN.L",
+        "headquarters": "Cambridge, UK",
+        "employees": "~90,000",
+        "2024_revenue": "$54.1B",
+        "therapeutic_areas": ["Oncology", "CVRM", "Respiratory & Immunology", "Rare Disease"],
+        "flagship_drugs": [
+            {"name": "Osimertinib (Tagrisso)", "target": "EGFR", "sales": "$6.8B", "phase": "Approved"},
+            {"name": "Durvalumab (Imfinzi)", "target": "PD-L1", "sales": "$4.5B", "phase": "Approved"},
+            {"name": "Trastuzumab deruxtecan (co-developed)", "target": "HER2", "sales": "$3.5B", "phase": "Approved"},
+            {"name": "Dapagliflozin (Farxiga)", "target": "SGLT2", "sales": "$7.1B", "phase": "Approved"},
+        ],
+        "pipeline_count": {"approved": 22, "phase_3": 12, "phase_2": 28, "phase_1": 18},
+        "recent_deals": [
+            "Acquired Gracell Biotechnologies (CAR-T) — $1.2B (2023)",
+            "Acquired Icosavax (RSV/hMPV vaccine) — $1.1B (2023)",
+            "Acquired Fusion Pharmaceuticals (radiopharma) — $2.4B (2024)",
+        ],
+        "strategy": "AZ's oncology portfolio is anchored by Tagrisso, Imfinzi, and Enhertu. "
+                     "Actively expanding into cell therapy, radiopharmaceuticals, and ADCs.",
+    },
+}
+
+MOCK_DISEASES = {
+    "NSCLC": {
+        "name": "Non-Small Cell Lung Cancer",
+        "global_incidence": "~2.2M new cases/year (2024)",
+        "mortality": "~1.8M deaths/year",
+        "5yr_survival": "Stage I: 65%, Stage IV: 8%",
+        "major_mutations": ["EGFR (15-20%)", "KRAS (25-30%)", "ALK (3-5%)",
+                           "ROS1 (1-2%)", "BRAF (1-3%)", "MET exon 14 (3%)", "RET (1-2%)"],
+        "approved_drugs_count": 45,
+        "drug_classes": ["TKIs (1st/2nd/3rd gen)", "Immune checkpoint inhibitors",
+                        "ADCs", "Bispecific antibodies", "Chemotherapy"],
+        "key_drugs": [
+            {"name": "Osimertinib", "target": "EGFR", "setting": "1L EGFRm ± adjuvant"},
+            {"name": "Pembrolizumab", "target": "PD-1", "setting": "1L PD-L1 ≥50% ± chemo"},
+            {"name": "Amivantamab", "target": "EGFR/MET", "setting": "ex20ins"},
+            {"name": "Sotorasib", "target": "KRAS G12C", "setting": "2L+"},
+            {"name": "Lorlatinib", "target": "ALK", "setting": "1L ALK+"},
+        ],
+        "market_size": "$32B (2024), projected $48B by 2030",
+        "pipeline": {"phase_3": 85, "phase_2": 150, "phase_1": 100},
+        "key_trends": ["Perioperative immunotherapy", "MRD-guided adjuvant therapy",
+                       "Antibody-drug conjugates expanding", "Bispecifics entering 1L"],
+    },
+    "Breast Cancer": {
+        "name": "Breast Cancer",
+        "global_incidence": "~2.3M new cases/year (2024)",
+        "mortality": "~685K deaths/year",
+        "subtypes": ["HR+/HER2- (70%)", "HER2+ (15-20%)", "TNBC (10-15%)"],
+        "approved_drugs_count": 52,
+        "key_drugs": [
+            {"name": "Trastuzumab deruxtecan", "target": "HER2", "setting": "HER2+ and HER2-low"},
+            {"name": "Sacituzumab govitecan", "target": "Trop-2", "setting": "TNBC, HR+/HER2-"},
+            {"name": "Palbociclib", "target": "CDK4/6", "setting": "HR+/HER2- 1L"},
+            {"name": "Olaparib", "target": "PARP", "setting": "BRCA1/2-mutated"},
+        ],
+        "market_size": "$28B (2024)",
+        "key_trends": ["CDK4/6 moving to adjuvant", "ADCs dominating HER2 space",
+                       "Immunotherapy for TNBC", "Oral SERDs entering market"],
+    },
+}
+
+MOCK_DRUG_SEARCH = {
+    # Returned by any drug search; keyed by target/disease
+    "default": [
+        {"name": "Osimertinib", "target": "EGFR", "type": "Small molecule",
+         "highest_phase": "Approved", "first_approved": "2015-11-13",
+         "indications": ["NSCLC"], "company": "AstraZeneca"},
+        {"name": "Gefitinib", "target": "EGFR", "type": "Small molecule",
+         "highest_phase": "Approved", "first_approved": "2003-05-05",
+         "indications": ["NSCLC"], "company": "AstraZeneca"},
+        {"name": "Cetuximab", "target": "EGFR", "type": "Monoclonal antibody",
+         "highest_phase": "Approved", "first_approved": "2004-02-12",
+         "indications": ["CRC", "HNSCC"], "company": "Merck KGaA / BMS"},
+        {"name": "Trastuzumab deruxtecan", "target": "HER2", "type": "ADC",
+         "highest_phase": "Approved", "first_approved": "2019-12-20",
+         "indications": ["Breast Cancer", "Gastric Cancer", "NSCLC"],
+         "company": "Daiichi Sankyo / AstraZeneca"},
+        {"name": "Pembrolizumab", "target": "PD-1", "type": "Monoclonal antibody",
+         "highest_phase": "Approved", "first_approved": "2014-09-04",
+         "indications": ["Melanoma", "NSCLC", "HNSCC", "cHL"], "company": "Merck (MSD)"},
+        {"name": "Amivantamab", "target": "EGFR/MET", "type": "Bispecific antibody",
+         "highest_phase": "Approved", "first_approved": "2021-05-21",
+         "indications": ["NSCLC (ex20ins)"], "company": "Janssen"},
+        {"name": "Sotorasib", "target": "KRAS G12C", "type": "Small molecule",
+         "highest_phase": "Approved", "first_approved": "2021-05-28",
+         "indications": ["NSCLC (KRAS G12C)"], "company": "Amgen"},
+        {"name": "Sacituzumab govitecan", "target": "Trop-2", "type": "ADC",
+         "highest_phase": "Approved", "first_approved": "2020-04-22",
+         "indications": ["TNBC", "HR+/HER2- Breast Cancer"], "company": "Gilead"},
+    ]
+}
+
+# =============================================================================
+# CSS — Product-grade styling
+# =============================================================================
+
+CUSTOM_CSS = """
+/* ===== Global ===== */
+:root {
+    --primary: #1a56db;
+    --primary-light: #3b82f6;
+    --primary-dark: #1e3a8a;
+    --accent: #059669;
+    --accent-light: #10b981;
+    --bg: #f8fafc;
+    --bg-card: #ffffff;
+    --text: #1e293b;
+    --text-secondary: #64748b;
+    --border: #e2e8f0;
+    --radius: 12px;
+    --shadow: 0 1px 3px rgba(0,0,0,0.08), 0 1px 2px rgba(0,0,0,0.06);
+    --shadow-lg: 0 10px 15px -3px rgba(0,0,0,0.08), 0 4px 6px -2px rgba(0,0,0,0.04);
+}
+
+.gradio-container {
+    max-width: 1200px !important;
+    margin: 0 auto !important;
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif !important;
+    background: var(--bg) !important;
+}
+
+/* ===== Header ===== */
+.header-container {
+    background: linear-gradient(135deg, var(--primary-dark) 0%, var(--primary) 100%);
+    border-radius: var(--radius);
+    padding: 32px 40px;
+    margin-bottom: 24px;
+    color: white;
+    position: relative;
+    overflow: hidden;
+}
+.header-container::after {
+    content: '';
+    position: absolute;
+    top: -50%;
+    right: -10%;
+    width: 300px;
+    height: 300px;
+    background: radial-gradient(circle, rgba(255,255,255,0.08) 0%, transparent 70%);
+    border-radius: 50%;
+}
+.header-title {
+    font-size: 28px;
+    font-weight: 700;
+    margin: 0 0 6px 0;
+    letter-spacing: -0.5px;
+}
+.header-subtitle {
+    font-size: 15px;
+    opacity: 0.85;
+    margin: 0;
+    font-weight: 400;
+}
+.header-badges {
+    display: flex;
+    gap: 10px;
+    margin-top: 14px;
+}
+.header-badge {
+    display: inline-flex;
+    align-items: center;
+    gap: 6px;
+    background: rgba(255,255,255,0.15);
+    backdrop-filter: blur(4px);
+    padding: 5px 12px;
+    border-radius: 20px;
+    font-size: 13px;
+    font-weight: 500;
+}
+
+/* ===== Tabs ===== */
+.tabs {
+    border: none !important;
+}
+.tab-nav {
+    background: var(--bg-card) !important;
+    border-radius: var(--radius) !important;
+    padding: 6px !important;
+    box-shadow: var(--shadow);
+    margin-bottom: 20px !important;
+    gap: 2px !important;
+}
+.tab-nav button {
+    border-radius: 10px !important;
+    padding: 10px 20px !important;
+    font-size: 14px !important;
+    font-weight: 500 !important;
+    border: none !important;
+    color: var(--text-secondary) !important;
+    transition: all 0.2s ease !important;
+    background: transparent !important;
+}
+.tab-nav button:hover {
+    background: #f1f5f9 !important;
+    color: var(--text) !important;
+}
+.tab-nav button.selected {
+    background: var(--primary) !important;
+    color: white !important;
+    box-shadow: 0 2px 8px rgba(26,86,219,0.25);
+}
+
+/* ===== Cards ===== */
+.card {
+    background: var(--bg-card);
+    border-radius: var(--radius);
+    padding: 24px;
+    box-shadow: var(--shadow);
+    border: 1px solid var(--border);
+    margin-bottom: 16px;
+    transition: box-shadow 0.2s ease;
+}
+.card:hover {
+    box-shadow: var(--shadow-lg);
+}
+
+/* ===== Chat ===== */
+.chat-container {
+    border-radius: var(--radius);
+    overflow: hidden;
+    box-shadow: var(--shadow);
+    border: 1px solid var(--border);
+    background: white;
+}
+.chat-message {
+    padding: 16px 20px;
+    border-bottom: 1px solid var(--border);
+}
+.chat-message.user {
+    background: #f0f9ff;
+}
+.chat-message.assistant {
+    background: white;
+}
+
+/* ===== Agent Thinking ===== */
+.thinking-steps {
+    background: #fffbeb;
+    border: 1px solid #fde68a;
+    border-radius: 10px;
+    padding: 12px 16px;
+    margin: 8px 0;
+    font-size: 13px;
+}
+.thinking-step {
+    padding: 4px 0;
+    color: #92400e;
+}
+
+/* ===== Inputs ===== */
+.agent-input textarea {
+    border-radius: 10px !important;
+    border: 2px solid var(--border) !important;
+    padding: 12px 16px !important;
+    font-size: 15px !important;
+    transition: border-color 0.2s ease !important;
+    resize: none !important;
+}
+.agent-input textarea:focus {
+    border-color: var(--primary-light) !important;
+    box-shadow: 0 0 0 3px rgba(59,130,246,0.15) !important;
+}
+
+/* ===== Buttons ===== */
+.btn-primary {
+    background: linear-gradient(135deg, var(--primary), var(--primary-light)) !important;
+    color: white !important;
+    border: none !important;
+    border-radius: 10px !important;
+    padding: 10px 24px !important;
+    font-weight: 600 !important;
+    cursor: pointer !important;
+    transition: all 0.2s ease !important;
+}
+.btn-primary:hover {
+    transform: translateY(-1px);
+    box-shadow: 0 4px 12px rgba(26,86,219,0.3);
+}
+
+/* ===== Example Chips ===== */
+.example-chips {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 8px;
+    margin: 12px 0;
+}
+
+/* ===== Stats Grid ===== */
+.stats-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+    gap: 12px;
+    margin: 16px 0;
+}
+
+/* ===== Report Content ===== */
+.report h2 { font-size: 22px; margin-top: 24px; color: var(--primary-dark); }
+.report h3 { font-size: 17px; margin-top: 18px; color: var(--text); }
+.report table { width: 100%; border-collapse: collapse; margin: 12px 0; font-size: 14px; }
+.report th { background: #f1f5f9; padding: 10px 12px; text-align: left; font-weight: 600; border-bottom: 2px solid var(--border); }
+.report td { padding: 8px 12px; border-bottom: 1px solid var(--border); }
+.report tr:hover td { background: #f8fafc; }
+
+/* ===== Status Badge ===== */
+.badge {
+    display: inline-block;
+    padding: 2px 10px;
+    border-radius: 12px;
+    font-size: 12px;
+    font-weight: 600;
+}
+.badge-approved { background: #dcfce7; color: #166534; }
+.badge-phase3  { background: #dbeafe; color: #1e40af; }
+.badge-phase2  { background: #fef3c7; color: #92400e; }
+.badge-phase1  { background: #fce7f3; color: #9d174d; }
+
+/* ===== Footer ===== */
+.footer {
+    text-align: center;
+    padding: 32px 16px;
+    color: var(--text-secondary);
+    font-size: 13px;
+    border-top: 1px solid var(--border);
+    margin-top: 40px;
+}
+
+/* ===== Loading ===== */
+.loading-pulse {
+    animation: pulse 2s cubic-bezier(0.4, 0, 0.6, 1) infinite;
+}
+@keyframes pulse {
+    0%, 100% { opacity: 1; }
+    50% { opacity: 0.5; }
+}
+
+/* ===== Animations ===== */
+@keyframes fadeIn {
+    from { opacity: 0; transform: translateY(8px); }
+    to { opacity: 1; transform: translateY(0); }
+}
+.fade-in { animation: fadeIn 0.4s ease-out; }
+
+/* Misc overrides */
+footer { display: none !important; }
+"""
+
+# =============================================================================
+# MCP CLIENT
+# =============================================================================
+
+_mcp_tools_cache: Optional[List[str]] = None
+
+async def get_mcp_tools() -> List[str]:
+    """Fetch available MCP tool names with caching."""
+    global _mcp_tools_cache
+    if _mcp_tools_cache is not None:
+        return _mcp_tools_cache
+    if not API_KEY:
+        return []
+    try:
+        from mcp import ClientSession
+        from mcp.client.streamable_http import streamablehttp_client
+        async with streamablehttp_client(SERVER_URL, timeout=15, sse_read_timeout=15) as (read, write, _):
+            async with ClientSession(read, write) as session:
+                await session.initialize()
+                result = await session.list_tools()
+                _mcp_tools_cache = [t.name for t in result.tools]
+                return _mcp_tools_cache
+    except Exception as e:
+        print(f"[MCP] Tool discovery failed: {e}")
+        return []
+
+
+async def call_mcp_tool(tool_name: str, args: dict) -> Optional[dict]:
+    """Call a specific MCP tool. Returns parsed JSON or None on failure."""
+    if not API_KEY:
+        return None
+    try:
+        from mcp import ClientSession
+        from mcp.client.streamable_http import streamablehttp_client
+        async with streamablehttp_client(SERVER_URL, timeout=30, sse_read_timeout=30) as (read, write, _):
+            async with ClientSession(read, write) as session:
+                await session.initialize()
+                result = await session.call_tool(tool_name, arguments=args)
+                if result.content:
+                    text = result.content[0].text
+                    return json.loads(text) if isinstance(text, str) else text
+    except Exception as e:
+        print(f"[MCP] {tool_name} failed: {e}")
+    return None
+
+
+# =============================================================================
+# AGENT ENGINE — Intent Parsing & Tool Routing
+# =============================================================================
+
+# Entity extraction patterns
+TARGET_KEYWORDS = {
+    "egfr": "EGFR", "her2": "HER2", "her-2": "HER2", "erbb2": "HER2",
+    "pd-l1": "PD-L1", "pdl1": "PD-L1", "pd-1": "PD-1", "pd1": "PD-1",
+    "braf": "BRAF", "alk": "ALK", "ros1": "ROS1", "kras": "KRAS",
+    "vegf": "VEGF", "vegfr": "VEGFR", "ctla-4": "CTLA-4", "ctla4": "CTLA-4",
+    "stat3": "STAT3", "met": "c-MET", "c-met": "c-MET", "ret": "RET",
+    "ntrk": "NTRK", "fgfr": "FGFR", "parp": "PARP", "cdk4": "CDK4/6",
+    "cdk4/6": "CDK4/6", "btk": "BTK", "jak": "JAK", "flt3": "FLT3",
+    "idh1": "IDH1", "idh2": "IDH2", "tp53": "TP53", "p53": "TP53",
+    "brca1": "BRCA1", "brca2": "BRCA2", "trop-2": "Trop-2", "trop2": "Trop-2",
+    "claudin": "Claudin 18.2", "cldn18": "Claudin 18.2",
+}
+
+DISEASE_KEYWORDS = {
+    "non-small cell lung cancer": "NSCLC", "nsclc": "NSCLC", "lung cancer": "Lung Cancer",
+    "breast cancer": "Breast Cancer", "triple-negative breast": "Triple-Negative Breast Cancer",
+    "tnbc": "Triple-Negative Breast Cancer",
+    "colorectal": "Colorectal Cancer", "crc": "Colorectal Cancer",
+    "melanoma": "Melanoma",
+    "pancreatic": "Pancreatic Cancer",
+    "hepatocellular": "Hepatocellular Carcinoma", "hcc": "Hepatocellular Carcinoma",
+    "liver cancer": "Hepatocellular Carcinoma",
+    "gastric": "Gastric Cancer", "stomach cancer": "Gastric Cancer",
+    "leukemia": "Leukemia", "lymphoma": "Lymphoma",
+    "ovarian": "Ovarian Cancer", "prostate": "Prostate Cancer",
+    "multiple myeloma": "Multiple Myeloma", "mm": "Multiple Myeloma",
+    "renal cell": "Renal Cell Carcinoma", "rcc": "Renal Cell Carcinoma",
+    "bladder": "Urothelial Carcinoma", "urothelial": "Urothelial Carcinoma",
+    "head and neck": "Head and Neck Cancer", "hnscc": "Head and Neck Cancer",
+    "glioblastoma": "Glioblastoma", "gbm": "Glioblastoma",
+    "alzheimer": "Alzheimer's Disease", "parkinson": "Parkinson's Disease",
+    "diabetes": "Diabetes", "obesity": "Obesity",
+}
+
+COMPANY_KEYWORDS = {
+    "roche": "Roche", "genentech": "Roche",
+    "novartis": "Novartis",
+    "pfizer": "Pfizer",
+    "merck": "Merck (MSD)", "msd": "Merck (MSD)",
+    "bristol-myers": "Bristol-Myers Squibb", "bms": "Bristol-Myers Squibb",
+    "astrazeneca": "AstraZeneca", "az": "AstraZeneca",
+    "johnson": "Johnson & Johnson", "jnj": "Johnson & Johnson", "janssen": "Johnson & Johnson",
+    "sanofi": "Sanofi",
+    "gsk": "GlaxoSmithKline",
+    "abbvie": "AbbVie",
+    "amgen": "Amgen",
+    "gilead": "Gilead",
+    "lilly": "Eli Lilly", "eli lilly": "Eli Lilly",
+    "moderna": "Moderna",
+    "biontech": "BioNTech",
+    "daiichi": "Daiichi Sankyo",
+    "beigene": "BeiGene", "百济": "BeiGene",
+    "innovent": "Innovent", "信达": "Innovent",
+    "hengrui": "Hengrui", "恒瑞": "Hengrui",
+    "akebia": "Akebia",
+}
+
+PHASE_KEYWORDS = {
+    "phase 3": "phase_3", "phase iii": "phase_3", "phase iii": "phase_3", "pivotal": "phase_3",
+    "phase 2": "phase_2", "phase ii": "phase_2",
+    "phase 1": "phase_1", "phase i": "phase_1", "first-in-human": "phase_1", "fih": "phase_1",
+    "approved": "approved", "marketed": "approved", "launched": "approved",
+    "preclinical": "preclinical",
+}
+
+MODULE_KEYWORDS = {
+    "target": ["target", "靶点", "receptor", "kinase", "protein", "gene", "mutation", "pathway",
+               "inhibitor target", "antibody target", "drug target"],
+    "drug": ["drug", "药物", "medicine", "inhibitor", "antibody", "therapy", "treatment regimen",
+             "approved drug", "pipeline drug", "molecule", "compound", "modality"],
+    "disease": ["disease", "疾病", "cancer", "tumor", "indication", "适应症", "epidemiology",
+                "patients", "prevalence", "incidence", "mortality"],
+    "company": ["company", "公司", "pharma", "biotech", "pipeline of", "portfolio",
+                "acquisition", "merger", "partner", "revenue"],
+    "trial": ["trial", "试验", "clinical", "nct", "enrollment", "endpoint", "randomized",
+              "double-blind", "phase 3 trial", "phase 2 trial", "phase 1 trial"],
+}
+
+
+def parse_intent(query: str) -> Dict:
+    """
+    Parse a natural language query to determine:
+    - module (target/drug/disease/company/trial)
+    - entities (targets, diseases, companies, phases)
+    - mcp_args (for direct MCP call)
+    - confidence
+    """
+    lower = query.lower()
+    result = {
+        "module": "target",  # default
+        "entities": {"targets": [], "diseases": [], "companies": [], "phases": []},
+        "mcp_args": {"limit": 10},
+        "confidence": 0.0,
+        "thinking": [],
+    }
+
+    # Extract entities
+    for kw, val in TARGET_KEYWORDS.items():
+        if kw in lower and val not in result["entities"]["targets"]:
+            result["entities"]["targets"].append(val)
+
+    for kw, val in DISEASE_KEYWORDS.items():
+        if kw in lower and val not in result["entities"]["diseases"]:
+            result["entities"]["diseases"].append(val)
+
+    for kw, val in COMPANY_KEYWORDS.items():
+        if kw in lower and val not in result["entities"]["companies"]:
+            result["entities"]["companies"].append(val)
+
+    for kw, val in PHASE_KEYWORDS.items():
+        if kw in lower:
+            result["entities"]["phases"].append(val)
+
+    # Determine module by scoring keyword matches
+    scores = {m: 0 for m in MODULE_KEYWORDS}
+    for module, keywords in MODULE_KEYWORDS.items():
+        for kw in keywords:
+            if kw in lower:
+                scores[module] += 1
+
+    best_module = max(scores, key=scores.get)
+    max_score = scores[best_module]
+
+    # Heuristic overrides based on entities found
+    if result["entities"]["targets"] and not result["entities"]["diseases"] and not result["entities"]["companies"]:
+        if max_score == 0 or best_module == "drug":
+            result["module"] = "target" if scores["target"] >= scores["drug"] else "drug"
+        else:
+            result["module"] = best_module
+    elif result["entities"]["diseases"] and not result["entities"]["targets"]:
+        result["module"] = "disease"
+    elif result["entities"]["companies"] and not result["entities"]["targets"]:
+        result["module"] = "company"
+    elif "trial" in lower or "clinical" in lower or "enrollment" in lower:
+        result["module"] = "trial"
+    else:
+        result["module"] = best_module if max_score > 0 else "target"
+
+    # Build MCP args based on module
+    mod = result["module"]
+    if mod == "target" and result["entities"]["targets"]:
+        result["mcp_args"] = {"target": result["entities"]["targets"][:3], "limit": 10}
+        result["thinking"].append(f"🔍 Detected target query → searching for: {', '.join(result['entities']['targets'][:3])}")
+    elif mod == "drug":
+        args = {"limit": 10}
+        if result["entities"]["targets"]:
+            args["target"] = result["entities"]["targets"][:3]
+        if result["entities"]["diseases"]:
+            args["disease"] = result["entities"]["diseases"][:3]
+        if result["entities"]["phases"]:
+            args["highest_phase"] = result["entities"]["phases"][:3]
+        result["mcp_args"] = args
+        result["thinking"].append(f"🔍 Detected drug query → searching with {json.dumps(args)}")
+    elif mod == "disease" and result["entities"]["diseases"]:
+        result["mcp_args"] = {"disease": result["entities"]["diseases"][:3], "limit": 10}
+        result["thinking"].append(f"🔍 Detected disease query → searching for: {', '.join(result['entities']['diseases'][:3])}")
+    elif mod == "company" and result["entities"]["companies"]:
+        result["mcp_args"] = {"company": result["entities"]["companies"][:3], "limit": 10}
+        result["thinking"].append(f"🔍 Detected company query → searching for: {', '.join(result['entities']['companies'][:3])}")
+    elif mod == "trial":
+        args = {"limit": 10}
+        if result["entities"]["targets"]:
+            args["target"] = result["entities"]["targets"][:3]
+        if result["entities"]["diseases"]:
+            args["disease"] = result["entities"]["diseases"][:3]
+        if result["entities"]["phases"]:
+            args["phase"] = result["entities"]["phases"][:3]
+        result["mcp_args"] = args
+        result["thinking"].append(f"🔍 Detected trial query → searching with {json.dumps(args)}")
+
+    # Confidence
+    entity_count = sum(len(v) for v in result["entities"].values())
+    result["confidence"] = min(entity_count * 0.25 + scores[result["module"]] * 0.15, 0.95)
+
+    return result
+
+
+# =============================================================================
+# REPORT BUILDERS
+# =============================================================================
+
+def _badge(phase: str) -> str:
+    """Generate an HTML badge for a drug phase."""
+    phase_lower = phase.lower()
+    if "approved" in phase_lower:
+        cls = "badge-approved"
+    elif "phase 3" in phase_lower or "phase iii" in phase_lower:
+        cls = "badge-phase3"
+    elif "phase 2" in phase_lower or "phase ii" in phase_lower:
+        cls = "badge-phase2"
+    elif "phase 1" in phase_lower or "phase i" in phase_lower:
+        cls = "badge-phase1"
+    else:
+        cls = "badge-phase2"
+    return f'<span class="badge {cls}">{phase}</span>'
+
+
+def build_target_report(target_data: Dict) -> str:
+    """Build a structured target intelligence report."""
+    name = target_data.get("name", "Unknown")
+    full = target_data.get("full_name", "")
+    family = target_data.get("family", "N/A")
+    cls = target_data.get("class", "N/A")
+    drugs = target_data.get("approved_drugs", [])
+    pipeline = target_data.get("pipeline_summary", {})
+    pathways = target_data.get("pathways", [])
+    mutations = target_data.get("mutation_hotspots", [])
+    landscape = target_data.get("competitive_landscape", "")
+
+    report = []
+    report.append(f"## 🎯 {name} — Target Intelligence Report")
+    report.append("")
+
+    # Overview card
+    report.append("### 📋 Overview")
+    report.append(f"| Property | Value |")
+    report.append(f"|----------|-------|")
+    report.append(f"| **Full Name** | {full} |")
+    report.append(f"| **Family** | {family} |")
+    report.append(f"| **Class** | {cls} |")
+    report.append(f"| **Approved Drugs** | {len(drugs)} |")
+    report.append("")
+
+    # Pathways
+    if pathways:
+        report.append("### 🧬 Signaling Pathways")
+        for p in pathways:
+            report.append(f"- {p}")
+        report.append("")
+
+    # Mutations
+    if mutations:
+        report.append("### 🔬 Key Mutations / Variants")
+        for m in mutations:
+            report.append(f"- {m}")
+        report.append("")
+
+    # Approved Drugs Table
+    if drugs:
+        report.append(f"### 💊 Approved Drugs ({len(drugs)})")
+        report.append("| Drug | Type | Generation | Year | Indications | Company |")
+        report.append("|------|------|-----------|------|-------------|---------|")
+        for d in drugs:
+            inds = ", ".join(d.get("indications", [])[:2])
+            if len(d.get("indications", [])) > 2:
+                inds += f" +{len(d['indications']) - 2} more"
+            report.append(f"| {d['name']} | {d['type']} | {d.get('gen', '-')} | "
+                         f"{d['year']} | {inds} | {d.get('company', '-')} |")
+        report.append("")
+
+    # Pipeline
+    if pipeline:
+        report.append("### 🔬 Pipeline Overview")
+        report.append(f"| Phase | Count |")
+        report.append(f"|-------|-------|")
+        for phase in ["phase_3", "phase_2", "phase_1", "preclinical"]:
+            label = phase.replace("_", " ").title()
+            report.append(f"| {label} | {pipeline.get(phase, 'N/A')} |")
+        report.append("")
+        hot = pipeline.get("hot_topics", [])
+        if hot:
+            report.append("**🔥 Hot Topics:**")
+            for t in hot:
+                report.append(f"- {t}")
+            report.append("")
+
+    # Competitive landscape
+    if landscape:
+        report.append("### 🏔️ Competitive Landscape")
+        report.append(landscape)
+        report.append("")
+
+    report.append("---")
+    report.append(f"*Report generated by PatSnap Pharma Intelligence Agent*")
+    return "\n".join(report)
+
+
+def build_drug_report(items: List[Dict], query_summary: str, total: int = 0) -> str:
+    """Build a drug pipeline report from search results."""
+    if not items:
+        return f"## 💊 Drug Search: {query_summary}\n\n📭 No results found. Try a different query."
+
+    drug_types = Counter()
+    companies = Counter()
+    years = []
+    phases = Counter()
+
+    for item in items:
+        drug_types[item.get("type", "Unknown")] += 1
+        companies[item.get("company", "Unknown")] += 1
+        phases[item.get("highest_phase", "Unknown")] += 1
+        date = item.get("first_approved", "")
+        if date and date != "N/A":
+            try:
+                years.append(int(date.split("-")[0]))
+            except (ValueError, IndexError):
+                pass
+
+    report = []
+    report.append(f"## 💊 {query_summary}")
+    report.append(f"*{len(items)} results{' of ' + str(total) if total else ''}*")
+    report.append("")
+
+    # Stats row
+    report.append("### 📊 Summary")
+    stats = []
+    if drug_types:
+        top = drug_types.most_common(3)
+        stats.append(f"**Top Types:** " + " · ".join(f"{t} ({c})" for t, c in top))
+    if phases:
+        top_p = phases.most_common(3)
+        stats.append(f"**Phases:** " + " · ".join(f"{p} ({c})" for p, c in top_p))
+    if years:
+        stats.append(f"**Timeline:** {min(years)} – {max(years)}")
+    if companies:
+        top_c = companies.most_common(3)
+        stats.append(f"**Top Companies:** " + " · ".join(f"{c} ({n})" for c, n in top_c))
+    for s in stats:
+        report.append(f"- {s}")
+    report.append("")
+
+    # Drug table
+    report.append("### 🧪 Pipeline Details")
+    report.append("| Drug | Type | Phase | Indications | Company |")
+    report.append("|------|------|-------|-------------|---------|")
+    for item in items[:15]:
+        name = item.get("name", "N/A")
+        dtype = item.get("type", "N/A")
+        phase = item.get("highest_phase", "N/A")
+        inds = ", ".join(item.get("indications", ["N/A"])[:2])
+        comp = item.get("company", "N/A")
+        report.append(f"| {name} | {dtype} | {_badge(phase)} | {inds} | {comp} |")
+    report.append("")
+
+    report.append("---")
+    report.append(f"*Report generated by PatSnap Pharma Intelligence Agent*")
+    return "\n".join(report)
+
+
+def build_disease_report(disease_data: Dict) -> str:
+    """Build a disease landscape report."""
+    name = disease_data.get("name", "Unknown")
+    report = []
+    report.append(f"## 🏥 {name} — Disease Landscape")
+    report.append("")
+
+    report.append("### 📊 Epidemiology")
+    for key in ["global_incidence", "mortality", "5yr_survival"]:
+        if key in disease_data:
+            label = key.replace("5yr", "5-Year ").replace("_", " ").title()
+            report.append(f"- **{label}:** {disease_data[key]}")
+    report.append("")
+
+    mutations = disease_data.get("major_mutations", [])
+    if mutations:
+        report.append("### 🧬 Key Driver Mutations")
+        for m in mutations:
+            report.append(f"- {m}")
+        report.append("")
+
+    key_drugs = disease_data.get("key_drugs", [])
+    if key_drugs:
+        report.append("### 💊 Key Therapies")
+        report.append("| Drug | Target | Setting |")
+        report.append("|------|--------|---------|")
+        for d in key_drugs:
+            report.append(f"| {d['name']} | {d['target']} | {d.get('setting', '-')} |")
+        report.append("")
+
+    report.append("### 📈 Market & Pipeline")
+    for key in ["market_size", "approved_drugs_count"]:
+        if key in disease_data:
+            label = key.replace("_", " ").title().replace("Drugs", "Drugs").replace("Count", "Count")
+            report.append(f"- **{label}:** {disease_data[key]}")
+    pipeline = disease_data.get("pipeline", {})
+    if pipeline:
+        report.append(f"- **Pipeline:** Phase 3: {pipeline.get('phase_3', 'N/A')} | "
+                     f"Phase 2: {pipeline.get('phase_2', 'N/A')} | Phase 1: {pipeline.get('phase_1', 'N/A')}")
+    report.append("")
+
+    trends = disease_data.get("key_trends", [])
+    if trends:
+        report.append("### 🔥 Key Trends")
+        for t in trends:
+            report.append(f"- {t}")
+        report.append("")
+
+    report.append("---")
+    report.append(f"*Report generated by PatSnap Pharma Intelligence Agent*")
+    return "\n".join(report)
+
+
+def build_company_report(company_data: Dict) -> str:
+    """Build a company profile report."""
+    name = company_data.get("name", "Unknown")
+    report = []
+    report.append(f"## 🏢 {name} — Company Profile")
+    report.append("")
+
+    report.append("### 📋 Company Overview")
+    for key in ["ticker", "headquarters", "employees", "2024_revenue"]:
+        if key in company_data:
+            label = key.replace("_", " ").title().replace("2024", "2024")
+            report.append(f"- **{label}:** {company_data[key]}")
+    report.append("")
+
+    ta = company_data.get("therapeutic_areas", [])
+    if ta:
+        report.append(f"**Therapeutic Areas:** " + " · ".join(ta))
+        report.append("")
+
+    flagship = company_data.get("flagship_drugs", [])
+    if flagship:
+        report.append("### 💊 Flagship Drugs")
+        report.append("| Drug | Target | Sales | Phase |")
+        report.append("|------|--------|-------|-------|")
+        for d in flagship:
+            report.append(f"| {d['name']} | {d['target']} | {d['sales']} | {_badge(d['phase'])} |")
+        report.append("")
+
+    pipeline = company_data.get("pipeline_count", {})
+    if pipeline:
+        report.append("### 🔬 Pipeline Overview")
+        report.append(f"| Phase | Count |")
+        report.append(f"|-------|-------|")
+        for phase in ["approved", "phase_3", "phase_2", "phase_1"]:
+            label = phase.replace("_", " ").title()
+            report.append(f"| {label} | {pipeline.get(phase, 'N/A')} |")
+        report.append("")
+
+    deals = company_data.get("recent_deals", [])
+    if deals:
+        report.append("### 🤝 Recent Strategic Deals")
+        for d in deals:
+            report.append(f"- {d}")
+        report.append("")
+
+    strategy = company_data.get("strategy", "")
+    if strategy:
+        report.append(f"### 🎯 Strategic Outlook\n{strategy}")
+        report.append("")
+
+    report.append("---")
+    report.append(f"*Report generated by PatSnap Pharma Intelligence Agent*")
+    return "\n".join(report)
+
+
+def build_agent_thinking(intent: Dict, mcp_available: bool, mcp_result: Optional[Dict]) -> str:
+    """Build the agent thinking trace HTML."""
+    steps = []
+    steps.append(f"🧠 **Intent:** {intent['module'].title()} query (confidence: {intent['confidence']:.0%})")
+
+    if intent["entities"]["targets"]:
+        steps.append(f"🎯 **Targets:** {', '.join(intent['entities']['targets'])}")
+    if intent["entities"]["diseases"]:
+        steps.append(f"🏥 **Diseases:** {', '.join(intent['entities']['diseases'])}")
+    if intent["entities"]["companies"]:
+        steps.append(f"🏢 **Companies:** {', '.join(intent['entities']['companies'])}")
+
+    for think in intent.get("thinking", []):
+        steps.append(think)
+
+    if mcp_available:
+        if mcp_result:
+            total = mcp_result.get("total", 0)
+            items = len(mcp_result.get("items", []))
+            steps.append(f"✅ **MCP Live Data:** Retrieved {items} of {total} records")
+        else:
+            steps.append(f"⚠️ **MCP:** No results from live API, using knowledge base")
+    else:
+        steps.append(f"📚 **Source:** Knowledge base (no API key configured)")
+
+    return "\n".join(f"<div class='thinking-step'>{s}</div>" for s in steps)
+
+
+# =============================================================================
+# CORE AGENT EXECUTION
+# =============================================================================
+
+async def run_agent(query: str, lang: str = "en") -> Tuple[str, str]:
+    """
+    Execute the full agent pipeline:
+    1. Parse intent
+    2. Try MCP → fallback to knowledge base
+    3. Build report
+
+    Returns (thinking_html, report_markdown)
+    """
+    if not query or not query.strip():
+        return "", "*👋 Welcome! Ask me anything about drug targets, diseases, companies, or clinical trials.*"
+
+    # Step 1: Parse intent
+    intent = parse_intent(query)
+    module = intent["module"]
+    mod_info = MODULES[module]
+
+    # Step 2: Try MCP
+    mcp_data = None
+    mcp_available = bool(API_KEY)
+    if mcp_available:
+        try:
+            from mcp import ClientSession
+            from mcp.client.streamable_http import streamablehttp_client
+            async with streamablehttp_client(SERVER_URL, timeout=25, sse_read_timeout=25) as (read, write, _):
+                async with ClientSession(read, write) as session:
+                    await session.initialize()
+                    tools = await session.list_tools()
+                    tool_names = [t.name for t in tools.tools]
+
+                    # Find matching tool
+                    tool_to_call = None
+                    for t in [mod_info["tool"], "ls_drug_search"]:  # fallback to drug search
+                        if t in tool_names:
+                            tool_to_call = t
+                            break
+
+                    if tool_to_call:
+                        result = await session.call_tool(tool_to_call, arguments=intent["mcp_args"])
+                        if result.content:
+                            text = result.content[0].text
+                            mcp_data = json.loads(text) if isinstance(text, str) else text
+        except Exception as e:
+            print(f"[Agent] MCP error: {e}")
+
+    # Step 3: Build thinking trace
+    if module == "target" and intent["entities"]["targets"]:
+        entity_name = intent["entities"]["targets"][0]
+    elif module == "disease" and intent["entities"]["diseases"]:
+        entity_name = intent["entities"]["diseases"][0]
+    elif module == "company" and intent["entities"]["companies"]:
+        entity_name = intent["entities"]["companies"][0]
+    else:
+        entity_name = query[:50]
+
+    thinking = build_agent_thinking(intent, mcp_available, mcp_data)
+
+    # Step 4: Build report
+    report = ""
+
+    if mcp_data and mcp_data.get("items"):
+        # Live MCP data
+        items = mcp_data["items"]
+        total = mcp_data.get("total", len(items))
+        report = build_drug_report(items, f"Results for \"{entity_name}\"", total)
+    elif module == "target":
+        # Knowledge base fallback for targets
+        target_key = entity_name.upper()
+        target_data = MOCK_TARGETS.get(target_key)
+        if target_data:
+            report = build_target_report(target_data)
+        else:
+            report = build_drug_report(
+                MOCK_DRUG_SEARCH["default"],
+                f"Results for \"{entity_name}\" (target overview)",
+                len(MOCK_DRUG_SEARCH["default"])
+            )
+    elif module == "disease":
+        # Knowledge base fallback for diseases
+        disease_key = None
+        for kw, val in DISEASE_KEYWORDS.items():
+            if val.upper() == entity_name.upper():
+                disease_key = val
+                break
+        if not disease_key:
+            disease_key = entity_name
+        disease_data = MOCK_DISEASES.get(disease_key)
+        if disease_data:
+            report = build_disease_report(disease_data)
+        else:
+            report = build_drug_report(
+                MOCK_DRUG_SEARCH["default"],
+                f"Results for \"{entity_name}\" (disease overview)",
+            )
+    elif module == "company":
+        # Knowledge base fallback for companies
+        company_data = MOCK_COMPANIES.get(entity_name)
+        if company_data:
+            report = build_company_report(company_data)
+        else:
+            report = f"## 🏢 {entity_name}\n\n*Detailed company profile not available in demo mode. "
+            report += "Connect a PatSnap API key for live data.*"
+    else:
+        # Default drug report from knowledge base
+        report = build_drug_report(
+            MOCK_DRUG_SEARCH["default"],
+            f"Results for \"{entity_name}\"",
+        )
+
+    return thinking, report
+
+
+# =============================================================================
+# GRADIO UI
+# =============================================================================
+
+def create_header():
+    """Create the app header block."""
+    return gr.HTML("""
+    <div class="header-container">
+        <h1 class="header-title">🧬 PatSnap Pharma Intelligence</h1>
+        <p class="header-subtitle">AI-powered drug discovery intelligence — explore targets, drugs, diseases, companies, and clinical trials.</p>
+        <div class="header-badges">
+            <span class="header-badge">🔬 Multi-Module Agent</span>
+            <span class="header-badge">📊 Live MCP Data</span>
+            <span class="header-badge">🤖 AI-Powered Reports</span>
+        </div>
+    </div>
+    """)
+
+
+def create_footer():
+    """Create the app footer."""
+    return gr.HTML("""
+    <div class="footer">
+        <strong>PatSnap Pharma Intelligence</strong> — Powered by PatSnap Life Sciences MCP<br>
+        <span style="opacity:0.7">Data sourced from PatSnap's pharmaceutical intelligence platform. Demo data shown where API key is not configured.</span>
+    </div>
+    """)
+
+
+def build_app():
+    """Build the complete Gradio application."""
+
+    with gr.Blocks(
+        title="PatSnap Pharma Intelligence",
+        analytics_enabled=False,
+    ) as app:
+        create_header()
+
+        # ===== TABS =====
+        with gr.Tabs(elem_classes="tabs"):
+
+            # ========== TAB 1: AGENT CHAT (Overview) ==========
+            with gr.TabItem("🤖 Agent Chat", id="chat"):
+                gr.Markdown(
+                    "### 💬 Ask anything about drug targets, diseases, or pharma companies.\n"
+                    "The AI agent will understand your intent, search relevant data, and generate a structured report.",
+                    elem_classes="fade-in"
+                )
+
+                with gr.Row(elem_classes="card"):
+                    # Chat layout
+                    with gr.Column(scale=3):
+                        chat_input = gr.Textbox(
+                            label="Your question",
+                            placeholder="e.g. \"Analyze EGFR as a drug target\" or \"What drugs target PD-L1?\" or \"Compare Roche and AstraZeneca oncology pipelines\"",
+                            lines=2,
+                            elem_classes="agent-input",
+                        )
+                        with gr.Row():
+                            chat_btn = gr.Button("🔍 Analyze", variant="primary", elem_classes="btn-primary")
+                            clear_btn = gr.Button("🗑️ Clear", variant="secondary", size="sm")
+
+                    with gr.Column(scale=1):
+                        gr.Markdown("#### ⚡ Quick Examples")
+                        example_btns = []
+                        examples = [
+                            ("🎯 EGFR", "Analyze EGFR as a drug target — approved drugs, pipeline, and competitive landscape"),
+                            ("💊 PD-1 drugs", "What drugs target PD-1? Show me approved and pipeline drugs"),
+                            ("🏥 NSCLC", "Give me a disease overview for non-small cell lung cancer"),
+                            ("🏢 Roche", "Profile Roche's oncology pipeline and recent deals"),
+                            ("🧪 ALK trials", "What clinical trials are targeting ALK in NSCLC?"),
+                            ("🔥 HER2 ADC", "Show me HER2-targeting antibody-drug conjugates"),
+                        ]
+                        with gr.Row():
+                            for i, (label, prompt) in enumerate(examples[:3]):
+                                btn = gr.Button(label, size="sm", scale=1)
+                                example_btns.append((btn, prompt))
+                        with gr.Row():
+                            for i, (label, prompt) in enumerate(examples[3:]):
+                                btn = gr.Button(label, size="sm", scale=1)
+                                example_btns.append((btn, prompt))
+
+                # Agent thinking (collapsible)
+                thinking_display = gr.HTML(
+                    value="<div class='thinking-steps'><div class='thinking-step'>🤖 Agent ready. Ask a question to begin.</div></div>",
+                    visible=True,
+                )
+
+                # Report output
+                report_display = gr.Markdown(
+                    value="### 👋 Welcome to PatSnap Pharma Intelligence\n\n"
+                          "I'm your AI agent for drug discovery intelligence. I can help with:\n\n"
+                          "- 🎯 **Target Intelligence** — Deep analysis of drug targets\n"
+                          "- 💊 **Drug Exploration** — Pipeline drugs by target, disease, or company\n"
+                          "- 🏥 **Disease Investigation** — Disease landscape & treatment overview\n"
+                          "- 🏢 **Company Profiling** — Pharma pipeline & strategic analysis\n"
+                          "- 🧪 **Clinical Trials** — Trial landscape by indication\n\n"
+                          "*Click a quick example above or type your question below.*",
+                    elem_classes="report",
+                )
+
+                # Wire up agent chat
+                async def handle_chat(query):
+                    thinking, report = await run_agent(query)
+                    thinking_html = f"<div class='thinking-steps'>{thinking}</div>"
+                    return thinking_html, report
+
+                chat_btn.click(
+                    fn=handle_chat,
+                    inputs=[chat_input],
+                    outputs=[thinking_display, report_display],
+                )
+
+                for btn, prompt in example_btns:
+                    btn.click(
+                        fn=lambda p=prompt: p,
+                        outputs=[chat_input],
+                    ).then(
+                        fn=handle_chat,
+                        inputs=[chat_input],
+                        outputs=[thinking_display, report_display],
+                    )
+
+                clear_btn.click(
+                    fn=lambda: ("", "", "### 👋 Welcome to PatSnap Pharma Intelligence\n\n*Ready for your next query.*"),
+                    outputs=[chat_input, thinking_display, report_display],
+                )
+
+            # ========== TAB 2-6: Structured Module Views ==========
+            # These reuse the same agent but with module-specific context
+            for mod_key, mod_info in MODULES.items():
+                icon = mod_info["icon"]
+                label = mod_info["label"]
+                desc = mod_info["desc"]
+
+                with gr.TabItem(f"{icon} {label}", id=mod_key):
+                    gr.Markdown(f"### {icon} {label}\n{desc}", elem_classes="fade-in")
+
+                    with gr.Row(elem_classes="card"):
+                        with gr.Column(scale=3):
+                            mod_input = gr.Textbox(
+                                label=f"Search {mod_info['entity']}",
+                                placeholder=f"Enter a {mod_info['entity']} name, e.g. EGFR, HER2, PD-L1...",
+                                lines=1,
+                                elem_classes="agent-input",
+                            )
+                            mod_btn = gr.Button(f"{icon} Search", variant="primary", elem_classes="btn-primary")
+
+                    mod_thinking = gr.HTML(visible=True)
+                    mod_report = gr.Markdown(
+                        value=f"### {icon} {label}\n\n*Enter a {mod_info['entity']} above to generate a report.*",
+                        elem_classes="report",
+                    )
+
+                    async def handle_module_search(q, mk=mod_key):
+                        if not q.strip():
+                            return "", f"### {MODULES[mk]['icon']} {MODULES[mk]['label']}\n\n*Please enter a search term.*"
+                        # Route to the appropriate module by prefixing the query
+                        routed_query = f"[{mk}] {q}"
+                        thinking, report = await run_agent(routed_query)
+                        thinking_html = f"<div class='thinking-steps'>{thinking}</div>"
+                        return thinking_html, report
+
+                    mod_btn.click(
+                        fn=handle_module_search,
+                        inputs=[mod_input],
+                        outputs=[mod_thinking, mod_report],
+                    )
+
+                    # Pre-made reports
+                    if mod_key == "target":
+                        gr.Markdown("#### 📊 Featured Target Reports")
+                        with gr.Row():
+                            for target_name in ["EGFR", "HER2", "PD-L1"]:
+                                td = MOCK_TARGETS.get(target_name)
+                                if td:
+                                    with gr.Column(scale=1):
+                                        with gr.Group(elem_classes="card"):
+                                            gr.Markdown(f"#### {target_name}\n{td.get('approved_drugs', [{}])[0].get('name', '')} ...")
+                                            btn = gr.Button(f"View {target_name} Report →", size="sm")
+                                            def show_report(tn=target_name):
+                                                td = MOCK_TARGETS.get(tn)
+                                                if td:
+                                                    return build_target_report(td)
+                                                return "Report not available."
+                                            btn.click(fn=show_report, outputs=[mod_report])
+
+                    elif mod_key == "company":
+                        gr.Markdown("#### 📊 Featured Company Reports")
+                        with gr.Row():
+                            for cname in ["Roche", "AstraZeneca"]:
+                                cd = MOCK_COMPANIES.get(cname)
+                                if cd:
+                                    with gr.Column(scale=1):
+                                        with gr.Group(elem_classes="card"):
+                                            gr.Markdown(f"#### {cname}\n{cd.get('headquarters', '')} — {cd.get('2024_revenue', '')}")
+                                            btn = gr.Button(f"View {cname} Report →", size="sm")
+                                            def show_crpt(cn=cname):
+                                                cd = MOCK_COMPANIES.get(cn)
+                                                if cd:
+                                                    return build_company_report(cd)
+                                                return "Report not available."
+                                            btn.click(fn=show_crpt, outputs=[mod_report])
+
+                    elif mod_key == "disease":
+                        gr.Markdown("#### 📊 Featured Disease Reports")
+                        with gr.Row():
+                            for dname in ["NSCLC", "Breast Cancer"]:
+                                dd = MOCK_DISEASES.get(dname)
+                                if dd:
+                                    with gr.Column(scale=1):
+                                        with gr.Group(elem_classes="card"):
+                                            gr.Markdown(f"#### {dname}\n{dd.get('global_incidence', '')}")
+                                            btn = gr.Button(f"View {dname} Report →", size="sm")
+                                            def show_drpt(dn=dname):
+                                                dd = MOCK_DISEASES.get(dn)
+                                                if dd:
+                                                    return build_disease_report(dd)
+                                                return "Report not available."
+                                            btn.click(fn=show_drpt, outputs=[mod_report])
+
+        # ===== FOOTER =====
+        create_footer()
+
+    return app
+
+
+# =============================================================================
+# ENTRY POINT
+# =============================================================================
+
+if __name__ == "__main__":
+    app = build_app()
+    app.queue(default_concurrency_limit=3, max_size=20)
+    app.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True,
+        css=CUSTOM_CSS,
+        theme=gr.themes.Soft(
+            primary_hue="blue",
+            secondary_hue="emerald",
+            neutral_hue="slate",
+        ),
+    )

fetch.py

@@ -0,0 +1,98 @@
+"""
+PatSnap MCP 数据采集脚本 — 在你本地 VS Code 里跑
+用法:
+  1. 把你的 API Key 设成环境变量: export PATSNAP_KEY="sk-xxx"
+  2. python fetch.py
+  3. 把生成的 real_data.json 内容发给我
+
+需要先装依赖: pip install mcp
+"""
+import json
+import asyncio
+import os
+import sys
+from mcp import ClientSession
+from mcp.client.streamable_http import streamablehttp_client
+
+# 从命令行参数或环境变量获取 key
+if len(sys.argv) > 1:
+    API_KEY = sys.argv[1]
+elif os.getenv("PATSNAP_KEY", ""):
+    API_KEY = os.getenv("PATSNAP_KEY", "")
+else:
+    print("❌ 用法: python fetch.py YOUR_API_KEY")
+    print("   或: set PATSNAP_KEY=你的key && python fetch.py")
+    sys.exit(1)
+
+SERVER_URL = f"https://connect.patsnap.com/096456/Logic-mcp?apikey={API_KEY}"
+
+# ============ 要查的内容 ============
+# 每个查询定义: (标签, 工具名, 参数)
+QUERIES = [
+    # --- Pharma Intelligence: 药物搜索 ---
+    ("EGFR_drugs", "ls_drug_search", {"target": ["EGFR"], "limit": 15}),
+    ("PD1_drugs", "ls_drug_search", {"target": ["PD-1"], "limit": 15}),
+    ("NSCLC_drugs", "ls_drug_search", {"disease": ["non-small cell lung cancer"], "limit": 15}),
+    ("bispecific_drugs", "ls_drug_search", {"drug_type": ["Bispecific antibody"], "limit": 10}),
+
+    # --- 如果还有可用工具，试试这些 (按需取消注释) ---
+    # ("EGFR_target_info", "ls_target_search", {"target": ["EGFR"], "limit": 5}),
+    # ("PDL1_clinical_trials", "ls_clinical_trial_search", {"target": ["PD-L1"], "limit": 10}),
+]
+
+# ============ 执行 ============
+async def fetch_one(label, tool, args):
+    print(f"\n{'='*60}")
+    print(f"🔍 {label}")
+    print(f"   Tool: {tool}")
+    print(f"   Args: {json.dumps(args)}")
+    
+    async with streamablehttp_client(SERVER_URL, timeout=60, sse_read_timeout=60) as (read, write, _):
+        async with ClientSession(read, write) as session:
+            await session.initialize()
+            
+            # 先看看有哪些工具可用
+            tools_result = await session.list_tools()
+            tool_names = [t.name for t in tools_result.tools]
+            print(f"   Available tools: {tool_names}")
+
+            if tool not in tool_names:
+                print(f"   ⚠️  Tool '{tool}' not found, skipping")
+                return None
+            
+            result = await session.call_tool(tool, arguments=args)
+            if result.content:
+                text = result.content[0].text
+                data = json.loads(text) if isinstance(text, str) else text
+                total = data.get("total", "?")
+                items = len(data.get("items", []))
+                print(f"   ✅ {items} items / {total} total")
+                return data
+    return None
+
+
+async def main():
+    results = {}
+    for label, tool, args in QUERIES:
+        try:
+            data = await fetch_one(label, tool, args)
+            if data:
+                results[label] = data
+        except Exception as e:
+            print(f"   ❌ Error: {e}")
+
+    # 保存
+    if results:
+        out_path = "real_data.json"
+        with open(out_path, "w", encoding="utf-8") as f:
+            json.dump(results, f, indent=2, ensure_ascii=False)
+        print(f"\n{'='*60}")
+        print(f"✅ 成功获取 {len(results)}/{len(QUERIES)} 个查询结果")
+        print(f"📁 已保存到: {out_path}")
+        print(f"📏 文件大小: {os.path.getsize(out_path)} bytes")
+        print(f"\n把 real_data.json 文件内容发给青崖即可！")
+    else:
+        print("\n❌ 没有任何结果，检查 API Key 或网络")
+
+if __name__ == "__main__":
+    asyncio.run(main())

fetch_real_data.py

@@ -0,0 +1,51 @@
+"""抓取 PatSnap MCP 真实数据，替换 HF Space demo 的 mock"""
+import json
+import asyncio
+import sys
+from mcp import ClientSession
+from mcp.client.streamable_http import streamablehttp_client
+
+API_KEY = sys.argv[1] if len(sys.argv) > 1 else ""
+SERVER_URL = f"https://connect.patsnap.com/096456/Logic-mcp?apikey={API_KEY}"
+
+QUERIES = [
+    ("EGFR", {"target": ["EGFR"], "limit": 10}),
+    ("PD-1", {"target": ["PD-1"], "limit": 10}),
+    ("NSCLC", {"disease": ["non-small cell lung cancer"], "limit": 10}),
+]
+
+async def fetch(label, args):
+    print(f"\n🔍 Fetching: {label} ...")
+    async with streamablehttp_client(SERVER_URL, timeout=30, sse_read_timeout=30) as (r, w, _):
+        async with ClientSession(r, w) as sess:
+            await sess.initialize()
+            res = await sess.call_tool("ls_drug_search", arguments=args)
+            if res.content:
+                txt = res.content[0].text
+                data = json.loads(txt) if isinstance(txt, str) else txt
+                print(f"  ✅ Got {data.get('total', 0)} records, {len(data.get('items', []))} items")
+                return data
+    return None
+
+async def main():
+    if not API_KEY:
+        print("❌ No API key. Usage: python fetch_real_data.py YOUR_KEY")
+        return
+
+    results = {}
+    for label, args in QUERIES:
+        try:
+            data = await fetch(label, args)
+            if data:
+                results[label] = data
+        except Exception as e:
+            print(f"  ❌ Failed: {e}")
+
+    if results:
+        with open("real_data.json", "w") as f:
+            json.dump(results, f, indent=2, ensure_ascii=False)
+        print(f"\n✅ Saved {len(results)} result sets to real_data.json")
+    else:
+        print("\n❌ No data fetched.")
+
+asyncio.run(main())

requirements.txt

@@ -0,0 +1,2 @@
+gradio>=4.0.0
+mcp