Update app.py

app.py

@@ -1,234 +1,254 @@
-"""
-Lab Report Decoder - Flask Application
-Professional web interface for lab report analysis
-"""
-
-from flask import Flask, render_template, request, jsonify, session
-from werkzeug.utils import secure_filename
-import os
-import tempfile
-import secrets
-from pdf_extractor import LabReportExtractor
-from rag_engine import LabReportRAG
-from dotenv import load_dotenv
-
-load_dotenv()
-
-app = Flask(__name__)
-app.secret_key = os.getenv('SECRET_KEY', secrets.token_hex(16))
-
-# Note: No OpenAI API key needed - using Hugging Face models!
-app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 16MB max file size
-app.config['UPLOAD_FOLDER'] = tempfile.gettempdir()
-
-# Initialize RAG system (singleton)
-rag_system = None
-
-def get_rag_system():
-    """Lazy load RAG system"""
-    global rag_system
-    if rag_system is None:
-        rag_system = LabReportRAG()
-    return rag_system
-
-@app.route('/')
-def index():
-    """Main page"""
-    return render_template('index.html')
-
-@app.route('/api/upload', methods=['POST'])
-def upload_file():
-    """Handle PDF upload and extraction"""
-    try:
-        if 'file' not in request.files:
-            return jsonify({'error': 'No file provided'}), 400
-        
-        file = request.files['file']
-        
-        if file.filename == '':
-            return jsonify({'error': 'No file selected'}), 400
-        
-        if not file.filename.lower().endswith('.pdf'):
-            return jsonify({'error': 'Only PDF files are allowed'}), 400
-        
-        # Save file temporarily
-        filename = secure_filename(file.filename)
-        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
-        file.save(filepath)
-        
-        try:
-            # Extract lab results
-            extractor = LabReportExtractor()
-            results = extractor.extract_from_pdf(filepath)
-            
-            if not results:
-                return jsonify({'error': 'No lab results found in PDF'}), 400
-            
-            # Convert to JSON-serializable format
-            results_data = [
-                {
-                    'test_name': r.test_name,
-                    'value': r.value,
-                    'unit': r.unit,
-                    'reference_range': r.reference_range,
-                    'status': r.status
-                }
-                for r in results
-            ]
-            
-            # Store in session
-            session['results'] = results_data
-            
-            return jsonify({
-                'success': True,
-                'results': results_data,
-                'count': len(results_data)
-            })
-        
-        finally:
-            # Clean up temp file
-            if os.path.exists(filepath):
-                os.remove(filepath)
-    
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-@app.route('/api/explain', methods=['POST'])
-def explain_results():
-    """Generate explanations for lab results"""
-    try:
-        results_data = session.get('results')
-        
-        if not results_data:
-            return jsonify({'error': 'No results found. Please upload a PDF first.'}), 400
-        
-        # Convert back to LabResult objects
-        from pdf_extractor import LabResult
-        results = [
-            LabResult(
-                test_name=r['test_name'],
-                value=r['value'],
-                unit=r['unit'],
-                reference_range=r['reference_range'],
-                status=r['status']
-            )
-            for r in results_data
-        ]
-        
-        # Generate explanations
-        rag = get_rag_system()
-        explanations = rag.explain_all_results(results)
-        
-        return jsonify({
-            'success': True,
-            'explanations': explanations
-        })
-    
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-@app.route('/api/ask', methods=['POST'])
-def ask_question():
-    """Answer follow-up questions"""
-    try:
-        data = request.get_json()
-        question = data.get('question', '').strip()
-        
-        if not question:
-            return jsonify({'error': 'No question provided'}), 400
-        
-        results_data = session.get('results')
-        
-        if not results_data:
-            return jsonify({'error': 'No results found. Please upload a PDF first.'}), 400
-        
-        # Convert back to LabResult objects
-        from pdf_extractor import LabResult
-        results = [
-            LabResult(
-                test_name=r['test_name'],
-                value=r['value'],
-                unit=r['unit'],
-                reference_range=r['reference_range'],
-                status=r['status']
-            )
-            for r in results_data
-        ]
-        
-        # Get answer
-        rag = get_rag_system()
-        answer = rag.answer_followup_question(question, results)
-        
-        return jsonify({
-            'success': True,
-            'question': question,
-            'answer': answer
-        })
-    
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-@app.route('/api/summary', methods=['GET'])
-def get_summary():
-    """Generate overall summary"""
-    try:
-        results_data = session.get('results')
-        
-        if not results_data:
-            return jsonify({'error': 'No results found. Please upload a PDF first.'}), 400
-        
-        # Convert back to LabResult objects
-        from pdf_extractor import LabResult
-        results = [
-            LabResult(
-                test_name=r['test_name'],
-                value=r['value'],
-                unit=r['unit'],
-                reference_range=r['reference_range'],
-                status=r['status']
-            )
-            for r in results_data
-        ]
-        
-        # Generate summary
-        rag = get_rag_system()
-        summary = rag.generate_summary(results)
-        
-        # Calculate statistics
-        stats = {
-            'total': len(results),
-            'normal': sum(1 for r in results if r.status == 'normal'),
-            'high': sum(1 for r in results if r.status == 'high'),
-            'low': sum(1 for r in results if r.status == 'low'),
-            'unknown': sum(1 for r in results if r.status == 'unknown')
-        }
-        
-        return jsonify({
-            'success': True,
-            'summary': summary,
-            'stats': stats
-        })
-    
-    except Exception as e:
-        return jsonify({'error': str(e)}), 500
-
-@app.route('/api/clear', methods=['POST'])
-def clear_session():
-    """Clear session data"""
-    session.clear()
-    return jsonify({'success': True})
-
-@app.errorhandler(413)
-def request_entity_too_large(error):
-    return jsonify({'error': 'File too large. Maximum size is 16MB.'}), 413
-
-@app.errorhandler(500)
-def internal_error(error):
-    return jsonify({'error': 'Internal server error'}), 500
-
-if __name__ == '__main__':
-    if not(os.path.isdir('chroma_db/')):
-         os.system("python build_vector_db.py")
-    #any available port
-    port = int(os.environ.get("PORT", 5000))
-    app.run(host="0.0.0.0", port=port)
+"""
+Lab Report Decoder - Flask Application
+Professional web interface for lab report analysis
+Fixed for Hugging Face Spaces stateless environment
+"""
+
+from flask import Flask, render_template, request, jsonify
+from werkzeug.utils import secure_filename
+import os
+import tempfile
+import secrets
+import json
+import uuid
+from pdf_extractor import LabReportExtractor, LabResult
+from rag_engine import LabReportRAG
+from dotenv import load_dotenv
+
+load_dotenv()
+
+app = Flask(__name__)
+app.secret_key = os.getenv('SECRET_KEY', secrets.token_hex(16))
+app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 16MB max file size
+app.config['UPLOAD_FOLDER'] = tempfile.gettempdir()
+
+# Initialize RAG system (singleton)
+rag_system = None
+
+# In-memory storage for sessions (better than Flask sessions in HF Spaces)
+session_storage = {}
+
+def get_rag_system():
+    """Lazy load RAG system"""
+    global rag_system
+    if rag_system is None:
+        print("🔄 Initializing RAG system...")
+        rag_system = LabReportRAG()
+        print("✅ RAG system ready")
+    return rag_system
+
+@app.route('/')
+def index():
+    """Main page"""
+    return render_template('index.html')
+
+@app.route('/api/upload', methods=['POST'])
+def upload_file():
+    """Handle PDF upload and extraction"""
+    try:
+        if 'file' not in request.files:
+            return jsonify({'error': 'No file provided'}), 400
+        
+        file = request.files['file']
+        
+        if file.filename == '':
+            return jsonify({'error': 'No file selected'}), 400
+        
+        if not file.filename.lower().endswith('.pdf'):
+            return jsonify({'error': 'Only PDF files are allowed'}), 400
+        
+        # Save file temporarily
+        filename = secure_filename(file.filename)
+        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+        file.save(filepath)
+        
+        try:
+            # Extract lab results
+            print("📄 Extracting lab results from PDF...")
+            extractor = LabReportExtractor()
+            results = extractor.extract_from_pdf(filepath)
+            
+            if not results:
+                return jsonify({'error': 'No lab results found in PDF. Please make sure your PDF contains a valid lab report with test names, values, and reference ranges.'}), 400
+            
+            print(f"✅ Extracted {len(results)} results")
+            
+            # Convert to JSON-serializable format
+            results_data = [
+                {
+                    'test_name': r.test_name,
+                    'value': r.value,
+                    'unit': r.unit,
+                    'reference_range': r.reference_range,
+                    'status': r.status
+                }
+                for r in results
+            ]
+            
+            # Generate a unique session ID
+            session_id = str(uuid.uuid4())
+            
+            # Store results in memory with session ID
+            session_storage[session_id] = {
+                'results': results_data,
+                'results_objects': results  # Store LabResult objects for later use
+            }
+            
+            print(f"💾 Stored results in session: {session_id}")
+            
+            return jsonify({
+                'success': True,
+                'session_id': session_id,
+                'results': results_data,
+                'count': len(results_data)
+            })
+        
+        finally:
+            # Clean up temp file
+            if os.path.exists(filepath):
+                os.remove(filepath)
+    
+    except Exception as e:
+        print(f"❌ Upload error: {str(e)}")
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/api/explain', methods=['POST'])
+def explain_results():
+    """Generate explanations for lab results"""
+    try:
+        data = request.get_json()
+        session_id = data.get('session_id')
+        
+        if not session_id or session_id not in session_storage:
+            return jsonify({'error': 'Session expired. Please upload your PDF again.'}), 400
+        
+        # Get results from storage
+        session_data = session_storage[session_id]
+        results = session_data['results_objects']
+        
+        print(f"🧠 Generating explanations for {len(results)} results...")
+        
+        # Generate explanations
+        rag = get_rag_system()
+        explanations = {}
+        
+        for i, result in enumerate(results):
+            print(f"  Explaining {i+1}/{len(results)}: {result.test_name}...")
+            try:
+                explanation = rag.explain_result(result)
+                explanations[result.test_name] = explanation
+            except Exception as e:
+                print(f"    Error: {str(e)}")
+                explanations[result.test_name] = f"Unable to generate explanation: {str(e)}"
+        
+        # Store explanations in session
+        session_storage[session_id]['explanations'] = explanations
+        
+        print("✅ All explanations generated")
+        
+        return jsonify({
+            'success': True,
+            'explanations': explanations
+        })
+    
+    except Exception as e:
+        print(f"❌ Explanation error: {str(e)}")
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/api/ask', methods=['POST'])
+def ask_question():
+    """Answer follow-up questions"""
+    try:
+        data = request.get_json()
+        question = data.get('question', '').strip()
+        session_id = data.get('session_id')
+        
+        if not question:
+            return jsonify({'error': 'No question provided'}), 400
+        
+        if not session_id or session_id not in session_storage:
+            return jsonify({'error': 'Session expired. Please upload your PDF again.'}), 400
+        
+        # Get results from storage
+        session_data = session_storage[session_id]
+        results = session_data['results_objects']
+        
+        print(f"💬 Answering question: {question}")
+        
+        # Get answer
+        rag = get_rag_system()
+        answer = rag.answer_followup_question(question, results)
+        
+        print("✅ Answer generated")
+        
+        return jsonify({
+            'success': True,
+            'question': question,
+            'answer': answer
+        })
+    
+    except Exception as e:
+        print(f"❌ Question error: {str(e)}")
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/api/summary', methods=['POST'])
+def get_summary():
+    """Generate overall summary"""
+    try:
+        data = request.get_json()
+        session_id = data.get('session_id')
+        
+        if not session_id or session_id not in session_storage:
+            return jsonify({'error': 'Session expired. Please upload your PDF again.'}), 400
+        
+        # Get results from storage
+        session_data = session_storage[session_id]
+        results = session_data['results_objects']
+        
+        print("📊 Generating summary...")
+        
+        # Generate summary
+        rag = get_rag_system()
+        summary = rag.generate_summary(results)
+        
+        # Calculate statistics
+        stats = {
+            'total': len(results),
+            'normal': sum(1 for r in results if r.status == 'normal'),
+            'high': sum(1 for r in results if r.status == 'high'),
+            'low': sum(1 for r in results if r.status == 'low'),
+            'unknown': sum(1 for r in results if r.status == 'unknown')
+        }
+        
+        print(f"✅ Summary generated - Stats: {stats}")
+        
+        return jsonify({
+            'success': True,
+            'summary': summary,
+            'stats': stats
+        })
+    
+    except Exception as e:
+        print(f"❌ Summary error: {str(e)}")
+        return jsonify({'error': str(e)}), 500
+
+@app.route('/api/health', methods=['GET'])
+def health_check():
+    """Health check endpoint"""
+    return jsonify({
+        'status': 'healthy',
+        'active_sessions': len(session_storage)
+    })
+
+@app.errorhandler(413)
+def request_entity_too_large(error):
+    return jsonify({'error': 'File too large. Maximum size is 16MB.'}), 413
+
+@app.errorhandler(500)
+def internal_error(error):
+    return jsonify({'error': 'Internal server error'}), 500
+
+if __name__ == '__main__':
+    print("🚀 Starting Lab Report Decoder...")
+    print("📍 Server will be available at http://0.0.0.0:7860")
+    app.run(debug=True, host='0.0.0.0', port=7860)