Update app.py

app.py

@@ -542,61 +542,89 @@ async def voice_chat_endpoint(
         logger.error(f"Error in voice chat: {e}")
         raise HTTPException(status_code=500, detail="Error processing voice chat")
 
-@app.post("/patient/upload-report")
-async def upload_patient_report(
-    patient_id: Optional[str] = Form(None),
-    file: UploadFile = File(...)
+@app.post("/analyze-report")
+async def analyze_clinical_report(
+    file: Optional[UploadFile] = File(None),
+    text: Optional[str] = Form(None),
+    temperature: float = Form(0.5),
+    max_new_tokens: int = Form(1024)
 ):
-    """Upload and analyze a patient report (text, PDF, or audio)."""
+    """
+    Analyze a clinical patient report either from uploaded file or direct text input.
+    
+    Parameters:
+    - file: Uploaded clinical report file (PDF, TXT, DOCX)
+    - text: Direct text input of the clinical report
+    - temperature: Controls randomness of response (0.1-1.0)
+    - max_new_tokens: Maximum length of response
+    
+    Returns structured analysis of the patient report.
+    """
     try:
-        # Validate patient_id if provided
-        if patient_id:
-            patient = await patients_collection.find_one({"fhir_id": patient_id})
-            if not patient:
-                raise HTTPException(status_code=404, detail=f"Patient {patient_id} not found")
-            logger.info(f"Uploading report for patient: {patient_id}")
-        else:
-            logger.info("Uploading report without patient ID")
-
-        # Determine file type from extension or MIME type
-        file_extension = file.filename.lower().split('.')[-1] if file.filename else ''
-        mime_type = file.content_type or mimetypes.guess_type(file.filename)[0] or 'application/octet-stream'
-        logger.info(f"Detected file extension: {file_extension}, MIME type: {mime_type}")
-
-        if file_extension in ['txt'] or mime_type.startswith('text'):
-            file_type = 'text'
-        elif file_extension == 'pdf' or mime_type == 'application/pdf':
-            file_type = 'pdf'
-        elif file_extension in ['wav', 'mp3', 'ogg', 'flac'] or mime_type.startswith('audio'):
-            file_type = 'audio'
-        else:
-            raise HTTPException(
-                status_code=400,
-                detail=f"Unsupported file type: {file_extension}. Supported: text (.txt), PDF (.pdf), audio (.wav, .mp3, .ogg, .flac)"
-            )
-
-        # Read and process file
-        file_content = await file.read()
-
-        if file_type == "text":
-            report_content = file_content.decode('utf-8')
-        elif file_type == "pdf":
-            report_content = extract_text_from_pdf(file_content)
-        elif file_type == "audio":
-            report_content = recognize_speech(file_content, language="en-US")
-        else:
-            raise HTTPException(status_code=400, detail="Error processing file type")
-
-        # Analyze the report
-        analysis = await analyze_patient_report(patient_id, report_content, file_type, file_content)
-        return JSONResponse(content=analysis)
-
+        # Validate input
+        if not (file or text):
+            raise HTTPException(status_code=400, detail="Either file or text input is required")
+        
+        # Extract text from file if provided
+        report_text = text
+        if file:
+            if file.filename.lower().endswith('.pdf'):
+                # PDF processing
+                import PyPDF2
+                pdf_reader = PyPDF2.PdfReader(file.file)
+                report_text = "\n".join([page.extract_text() for page in pdf_reader.pages])
+            elif file.filename.lower().endswith(('.txt', '.md')):
+                # Plain text
+                report_text = (await file.read()).decode('utf-8')
+            elif file.filename.lower().endswith(('.docx', '.doc')):
+                # Word document
+                from docx import Document
+                doc = Document(io.BytesIO(await file.read()))
+                report_text = "\n".join([para.text for para in doc.paragraphs])
+            else:
+                raise HTTPException(status_code=400, detail="Unsupported file format")
+        
+        # Clean and validate the extracted text
+        if not report_text or len(report_text.strip()) < 50:
+            raise HTTPException(status_code=400, detail="Report text is too short or empty")
+        
+        # Create analysis prompt
+        prompt = (
+            "You are a senior clinical analyst. Analyze this patient report and provide:\n"
+            "1. SUMMARY: Concise summary of key findings\n"
+            "2. DIAGNOSES: List of confirmed or suspected diagnoses\n"
+            "3. RISK FACTORS: Important risk factors identified\n"
+            "4. RED FLAGS: Any urgent concerns that need attention\n"
+            "5. RECOMMENDATIONS: Suggested next steps for care\n\n"
+            f"PATIENT REPORT:\n{report_text[:15000]}"  # Limit input size
+        )
+        
+        # Get AI analysis
+        raw_response = agent.chat(
+            message=prompt,
+            history=[],
+            temperature=temperature,
+            max_new_tokens=max_new_tokens
+        )
+        
+        # Structure the response
+        structured_response = structure_medical_response(raw_response)
+        
+        # Add suicide risk assessment
+        risk_level, risk_score, risk_factors = detect_suicide_risk(raw_response)
+        structured_response["suicide_risk"] = {
+            "level": risk_level.value,
+            "score": risk_score,
+            "factors": risk_factors
+        }
+        
+        return JSONResponse(content=structured_response)
+    
     except HTTPException:
         raise
     except Exception as e:
-        logger.error(f"Error processing patient report upload: {e}")
-        raise HTTPException(status_code=500, detail="Error processing patient report")
-
+        logger.error(f"Error analyzing clinical report: {e}")
+        raise HTTPException(status_code=500, detail="Error processing clinical report")
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=8000)