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README.md

@@ -1,12 +1,35 @@
 ---
-title: Citation Generator
-emoji: 🌍
-colorFrom: red
+title: Clinical Diagnosis Explorer
+emoji: 🏥
+colorFrom: blue
 colorTo: green
 sdk: gradio
 sdk_version: 5.34.1
-app_file: gradio_ui_enhanced.py
+app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Clinical Diagnosis Explorer
+
+Extract diagnostic claims and ground them in the scientific literature with PubMed search.
+
+## Features
+
+- **Diagnosis-First Approach**: Automatically extracts primary diagnosis and related clinical claims
+- **Automated PubMed Search**: Generates optimized search queries and retrieves relevant research
+- **Evidence Analysis**: Uses GPT-4o to analyze research evidence and assess claim validity
+- **Citation Integration**: Generates revised clinical notes with inline citations
+
+## Usage
+
+1. Enter a clinical diagnosis or clinical note
+2. Click "Extract Claims & Search Literature"
+3. Review the evidence-based analysis with citations
+
+## Example Input
+
+```
+28-year-old female with progressive word comprehension deficits, surface dyslexia, and difficulty naming objects while speech remains fluent. MRI reveals bilateral anterior temporal lobe atrophy. Diagnosis: Semantic Variant Primary Progressive Aphasia (svPPA).
+```
+
+

app.py

@@ -1,18 +1,246 @@
 import gradio as gr
-from anthropic import Anthropic
+import os
+import sys
+from typing import Dict, Any
+import json
 
-def get_api_key(api_key):
-    return f"API Key received: {api_key}"
+# Import the ENHANCED clinical diagnosis exploration functions
+from clinical_diag_exp_enhanced import run_clinical_diagnosis_exploration_enhanced
 
-iface = gr.Interface(
-    fn=get_api_key,
-    inputs=gr.Textbox(label="Enter your Anthropic API key here:"),
-    outputs=gr.Textbox(label="Response"),
-    title="Anthropic API Key"
+# Get API key from environment variable
+try:
+    API_KEY = os.environ['OPENAI_API_KEY']
+    if not API_KEY:
+        raise KeyError("OPENAI_API_KEY is empty")
+except KeyError:
+    print("ERROR: OPENAI_API_KEY environment variable not found or empty.")
+    print("Please set your OpenAI API key as an environment variable:")
+    print("export OPENAI_API_KEY='your-api-key-here'")
+    sys.exit(1)
+
+
+def run_clinical_exploration_enhanced(diagnosis_text: str, max_results: int, max_claims: int) -> Dict[str, Any]:
+    """
+    Run the ENHANCED clinical diagnosis exploration pipeline.
+    """
+    if not diagnosis_text or diagnosis_text.strip() == "":
+        return {
+            "error": "Please enter a clinical diagnosis or note to analyze."
+        }
+    
+    try:
+        # Run the ENHANCED clinical diagnosis exploration
+        results = run_clinical_diagnosis_exploration_enhanced(
+            diagnosis_text=diagnosis_text.strip(),
+            max_results_per_claim=max_results,
+            max_claims=max_claims
+        )
+        
+        # Format the results for display
+        formatted_results = {
+            "diagnosis": results.get("diagnosis", "No diagnosis extracted"),
+            "revised_note": results.get("revised_note", "No revised note generated"),
+            "total_claims": results.get("claims_researched", 0),
+            "total_articles": results.get("total_articles", 0),
+            "total_llm_calls": results.get("total_llm_calls", 0),
+            "claims_summary": "",
+            "references": results.get("references", "No references available."),
+            "missing_information": results.get("missing_information", []),
+            "enhancement": results.get("enhancement", "Enhanced features"),
+        }
+        
+        # Create a detailed summary of claims with full research summaries and references
+        claims = results.get("claims", [])
+        claims_summary = "## Claims Analyzed:\n\n"
+        for i, claim in enumerate(claims, 1):
+            claims_summary += f"### Claim {i}: {claim['claim_text']}\n"
+            claims_summary += f"- **Type:** {claim['claim_type']}\n"
+            claims_summary += f"- **Evidence Level:** {claim['evidence_level']}\n"
+            claims_summary += f"- **Research Status:** {claim['research_status']}\n"
+            claims_summary += f"- **Evidence Quality:** {claim.get('evidence_quality', 'unknown')}\n"
+            claims_summary += f"- **Articles Found:** {len(claim['abstracts'])}\n"
+            claims_summary += f"- **PubMed Query:** `{claim['pubmed_query']}`\n"
+            claims_summary += f"- **Query Strategy:** {claim.get('query_strategy', 'Standard query')}\n\n"
+            
+            # Add the detailed summary with inline citations
+            claims_summary += f"**Detailed Summary:** {claim['detailed_summary']}\n\n"
+            claims_summary += f"**Brief Summary:** {claim['evidence_summary']}\n\n"
+            
+            # Add critical assessment
+            if claim.get('critical_assessment'):
+                claims_summary += f"**Critical Assessment:** {claim['critical_assessment']}\n\n"
+            
+            # Add supporting/contradicting evidence if available
+            if claim.get('supporting_evidence'):
+                claims_summary += "**Supporting Evidence:**\n"
+                for evidence in claim['supporting_evidence']:
+                    claims_summary += f"- {evidence}\n"
+                claims_summary += "\n"
+            
+            if claim.get('contradicting_evidence'):
+                claims_summary += "**Contradicting Evidence:**\n"
+                for evidence in claim['contradicting_evidence']:
+                    claims_summary += f"- {evidence}\n"
+                claims_summary += "\n"
+            
+            # Add methodological issues if available
+            if claim.get('methodological_issues'):
+                claims_summary += "**Methodological Issues:**\n"
+                for issue in claim['methodological_issues']:
+                    claims_summary += f"- {issue}\n"
+                claims_summary += "\n"
+            
+            # Add the claim-specific references
+            claims_summary += f"**Claim-Specific References:**\n{claim['claim_references']}\n\n"
+            
+            claims_summary += "---\n\n"
+        
+        formatted_results["claims_summary"] = claims_summary
+        
+        return formatted_results
+        
+    except Exception as e:
+        return {
+            "error": f"Error running clinical exploration: {str(e)}"
+        }
+
+def create_interface():
+    """
+    Create the Clinical Diagnosis Explorer interface for extracting diagnostic claims 
+    and grounding them in scientific literature with PubMed search.
+    """
     
-)
+    with gr.Blocks(title="Clinical Diagnosis Explorer", theme=gr.themes.Soft()) as iface:
+        
+        gr.Markdown("# Clinical Diagnosis Explorer")
+        gr.Markdown("**Extract diagnostic claims and ground them in the scientific literature with PubMed search**")
+        gr.Markdown("*Features: Diagnosis-first approach, fuzzy search, LLM analysis, evidence-based clinical notes*")
+        
+        with gr.Tab("Clinical Analysis"):
+            gr.Markdown("## Analyze Clinical Diagnosis")
+            gr.Markdown("Enter a clinical diagnosis or clinical note to extract diagnostic claims and validate each against medical literature using PubMed search.")
+            
+            diagnosis_input = gr.Textbox(
+                label="Clinical Diagnosis or Note",
+                placeholder="Enter the clinical diagnosis, clinical note, or diagnostic reasoning here...\n\nExample: 28-year-old right-handed female with an 18-month history of progressive word comprehension deficits, surface dyslexia, and difficulty naming common objects while speech remains fluent and grammatically intact. MRI reveals bilateral anterior temporal lobe atrophy. Diagnosis: Semantic Variant Primary Progressive Aphasia (svPPA).",
+                lines=12
+            )
+            
+            with gr.Row():
+                max_results = gr.Slider(
+                    minimum=5,
+                    maximum=20,
+                    value=12,
+                    step=1,
+                    label="Max Results per Claim",
+                    info="Number of PubMed articles to analyze per claim (increased for better analysis)"
+                )
+                
+                max_claims = gr.Slider(
+                    minimum=3,
+                    maximum=12,
+                    value=8,
+                    step=1,
+                    label="Max Claims to Extract",
+                    info="Maximum number of claims to extract from diagnosis"
+                )
+            
+            analyze_btn = gr.Button("🔍 Extract Claims & Search Literature", variant="primary")
+
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("### Results")
+                    results_output = gr.JSON(label="Analysis Results", visible=False)
+                    error_output = gr.Textbox(label="Error", visible=False)
+                
+                with gr.Column():
+                    gr.Markdown("### Analysis Summary")
+                    summary_output = gr.Markdown(label="Analysis Summary")
+            
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("### Primary Diagnosis")
+                    diagnosis_output = gr.Markdown(label="Extracted Diagnosis")
+                
+                with gr.Column():
+                    gr.Markdown("### Missing Information")
+                    missing_info_output = gr.Markdown(label="Missing Information")
+            
+            with gr.Row():
+                gr.Markdown("### Revised Clinical Note")
+                revised_note_output = gr.Markdown(label="Evidence-Based Clinical Note")
+            
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("### Claims Analysis")
+                    claims_output = gr.Markdown(label="Detailed Claims Analysis")
+                
+                with gr.Column():
+                    gr.Markdown("### References")
+                    references_output = gr.Markdown(label="Research References")
+            
+            def process_enhanced_analysis(diagnosis, max_res, max_clms):
+                results = run_clinical_exploration_enhanced(diagnosis, max_res, max_clms)
+                
+                if "error" in results:
+                    return {
+                        results_output: gr.update(value=None, visible=False),
+                        error_output: gr.update(value=results["error"], visible=True),
+                        summary_output: gr.update(value=""),
+                        diagnosis_output: gr.update(value=""),
+                        missing_info_output: gr.update(value=""),
+                        revised_note_output: gr.update(value=""),
+                        claims_output: gr.update(value=""),
+                        references_output: gr.update(value=""),
+                    }
+                else:
+                    # Format analysis summary
+                    summary_text = f"""
+## Analysis Summary
+
+**Total Claims Researched:** {results.get("total_claims", 0)}
+**Total Articles Found:** {results.get("total_articles", 0)}
+**Total LLM Calls:** {results.get("total_llm_calls", 0)}
+**Enhanced Features:** {results.get("enhancement", "Enhanced analysis")}
+"""
+                    
+                    # Format diagnosis
+                    diagnosis_text = f"""
+## Primary Diagnosis
 
+**Extracted Diagnosis:** {results.get("diagnosis", "No diagnosis extracted")}
 
+This diagnosis was identified as the primary clinical condition and used as the foundation for claim extraction and PubMed query generation.
+"""
+                    
+                    # Format missing information
+                    missing_info = results.get("missing_information", [])
+                    missing_text = "## Missing Information\n\n"
+                    if missing_info:
+                        for i, info in enumerate(missing_info, 1):
+                            missing_text += f"{i}. ❓ {info}\n"
+                    else:
+                        missing_text += "No missing information identified."
+                    
+                    return {
+                        results_output: gr.update(value=results, visible=True),
+                        error_output: gr.update(value="", visible=False),
+                        summary_output: gr.update(value=summary_text),
+                        diagnosis_output: gr.update(value=diagnosis_text),
+                        missing_info_output: gr.update(value=missing_text),
+                        revised_note_output: gr.update(value=results["revised_note"]),
+                        claims_output: gr.update(value=results["claims_summary"]),
+                        references_output: gr.update(value=results["references"]),
+                    }
+            
+            analyze_btn.click(
+                fn=process_enhanced_analysis,
+                inputs=[diagnosis_input, max_results, max_claims],
+                outputs=[results_output, error_output, summary_output, diagnosis_output, missing_info_output, revised_note_output, claims_output, references_output]
+            )
+            
+    return iface
 
 if __name__ == "__main__":
-    iface.launch(share=True)
+    iface = create_interface()
+    iface.launch()

clinical_diag_exp_enhanced.py

@@ -9,6 +9,7 @@ import os
 from typing import List, Dict, Tuple
 import xml.etree.ElementTree as ET
 import re
+import sys
 
 # Import functions from simple_pubmed.py
 from simple_pubmed import (
@@ -21,9 +22,18 @@ from simple_pubmed import (
     create_fallback_search
 )
 
-os.environ["OPENAI_API_KEY"] = ""
+# Get API key from environment variable
+try:
+    API_KEY = os.environ['OPENAI_API_KEY']
+    if not API_KEY:
+        raise KeyError("OPENAI_API_KEY is empty")
+except KeyError:
+    print("ERROR: OPENAI_API_KEY environment variable not found or empty.")
+    print("Please set your OpenAI API key as an environment variable:")
+    print("export OPENAI_API_KEY='your-api-key-here'")
+    sys.exit(1)
 
-client = OpenAI()
+client = OpenAI(api_key=API_KEY)
 
 #%%
 # ENHANCED: Diagnosis-first claim extraction with fuzzy search
@@ -139,7 +149,7 @@ def extract_clinical_claims_and_gram(diagnosis_text: str, max_claims: int = 12)
     
     try:
         response = client.chat.completions.create(
-            model="gpt-4o",  # ENHANCED: Use GPT-4o for better analysis
+            model="gpt-4.1",  
             messages=messages,
             tools=tools,
             tool_choice={"type": "function", "function": {"name": "create_diagnosis_and_claims"}}
@@ -181,7 +191,6 @@ def extract_clinical_claims_and_gram(diagnosis_text: str, max_claims: int = 12)
 def research_claim_enhanced(claim: Dict, max_results: int = 12) -> Dict:
     """
     ENHANCED: Research claim with more rigorous evidence evaluation and fuzzy search.
-    Uses GPT-4o for better analysis and is willing to say "no" when evidence doesn't support claims.
     
     Args:
         claim (Dict): Claim with PubMed query
@@ -386,7 +395,6 @@ def fetch_pubmed_abstracts_direct(pmids: List[str]) -> List[Dict]:
 
 def generate_enhanced_summary_and_analysis(claim: Dict, abstracts: List[Dict]) -> Dict:
     """
-    ENHANCED: Comprehensive analysis with GPT-4o for better rigor.
     Willing to say "no" when evidence doesn't support claims.
     """
     
@@ -501,7 +509,7 @@ def generate_enhanced_summary_and_analysis(claim: Dict, abstracts: List[Dict]) -
     
     try:
         response = client.chat.completions.create(
-            model="gpt-4o",  # ENHANCED: Use GPT-4o for better analysis
+            model="gpt-4.1",  # ENHANCED: Use GPT-4o for better analysis
             messages=messages,
             tools=tools,
             tool_choice={"type": "function", "function": {"name": "create_enhanced_analysis"}}
@@ -659,7 +667,7 @@ def generate_revised_clinical_note(original_note: str, researched_claims: List[D
     
     try:
         response = client.chat.completions.create(
-            model="gpt-4o",  # ENHANCED: Use GPT-4o for better note revision
+            model="gpt-4.1",  # ENHANCED: Use GPT-4o for better note revision
             messages=messages,
             tools=tools,
             tool_choice={"type": "function", "function": {"name": "create_revised_note"}}
@@ -686,7 +694,7 @@ def run_clinical_diagnosis_exploration_enhanced(diagnosis_text: str, max_results
     ENHANCEMENTS:
     1. Diagnosis-first claim extraction approach
     2. Fuzzy search with multiple fallback strategies
-    3. Enhanced evidence analysis with GPT-4o
+    3. Enhanced evidence analysis
     4. Willingness to say "no" when evidence doesn't support claims
     5. Revised clinical note output with citations and evidence
     6. Better cost-quality balance (increased cost for better results)
@@ -768,7 +776,7 @@ def run_clinical_diagnosis_exploration_enhanced(diagnosis_text: str, max_results
         "total_articles": len(unique_abstracts),
         "claims_researched": len(researched_claims),
         "missing_information": claims_analysis['missing_information'],
-        "enhancement": "GPT-4o analysis, diagnosis-first approach, fuzzy search, revised note output"
+        "enhancement": "diagnosis-first approach, fuzzy search, revised note output"
     }
     
     print("=== ENHANCED PIPELINE COMPLETE ===")
@@ -776,7 +784,7 @@ def run_clinical_diagnosis_exploration_enhanced(diagnosis_text: str, max_results
     print(f"Claims researched: {results['claims_researched']}")
     print(f"Total articles: {results['total_articles']}")
     print(f"Missing information identified: {len(results['missing_information'])}")
-    print(f"Enhanced features: Diagnosis-first approach, fuzzy search, revised note output, GPT-4o analysis")
+    print(f"Enhanced features: Diagnosis-first approach, fuzzy search, revised note output")
     print()
     
     return results

requirements.txt

@@ -1,37 +1,18 @@
-# Core dependencies for clinical diagnosis exploration tools
+# Core dependencies for clinical diagnosis exploration
 openai>=1.0.0
-anthropic>=0.8.0
 
 # Data processing and analysis
 pandas>=2.0.0
-numpy>=1.24.0
-scipy>=1.10.0
 
 # Web scraping and API interactions
 requests>=2.28.0
 beautifulsoup4>=4.11.0
 
-# Machine learning and statistical analysis
-scikit-learn>=1.2.0
-matplotlib>=3.7.0
-seaborn>=0.12.0
-statsmodels>=0.13.0
-
 # Web interface
 gradio>=4.0.0
 
-# Environment and configuration
-python-dotenv>=0.19.0
-
 # Type hints and utilities
 typing-extensions>=4.0.0
-pathlib>=1.0.1
-
-# JSON schema validation
-jsonschema>=4.17.3
 
-# Development and testing (optional)
-pytest>=6.0
-black>=22.0
-isort>=5.0
-mypy>=0.900 
+# JSON schema validation (for LLM function calling)
+jsonschema>=4.17.3

simple_pubmed.py

@@ -9,10 +9,20 @@ import os
 from typing import List, Dict
 import xml.etree.ElementTree as ET
 import re
+import sys
 
-os.environ["OPENAI_API_KEY"] = ""
+# Get API key from environment variable
+try:
+    API_KEY = os.environ['OPENAI_API_KEY']
+    if not API_KEY:
+        raise KeyError("OPENAI_API_KEY is empty")
+except KeyError:
+    print("ERROR: OPENAI_API_KEY environment variable not found or empty.")
+    print("Please set your OpenAI API key as an environment variable:")
+    print("export OPENAI_API_KEY='your-api-key-here'")
+    sys.exit(1)
 
-client = OpenAI()
+client = OpenAI(api_key=API_KEY)
 
 # Helper function to safely parse JSON from LLM function calls
 def safe_json_loads(json_string: str) -> dict: