Update backend.py (#52)

- Update backend.py (6213b11ef7a2345f46c3f331ea1b3ae9c719d25b)


Co-authored-by: ashrith repaka <godtier812@users.noreply.huggingface.co>

backend.py

@@ -74,7 +74,6 @@ safety_settings = [
     {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
 ]
 
-# --- Pydantic Models for API Endpoints ---
 class ChatRequest(BaseModel):
     user_id: Optional[str] = "anonymous"
     question: str
@@ -86,27 +85,18 @@ class TextRequest(BaseModel):
     text: str
 
 system_prompt = """ You are a highly skilled medical practitioner specializing in medical image and document analysis. You will be given either a medical image or a PDF. 
-
 Your responsibilities are:
-
 1. **Extract Text**: If the input is a PDF or image, first extract all the text content (lab values, notes, measurements, etc.). Do not summarize — keep the extracted text verbatim.  
-
 2. **Detailed Analysis**: Use both the extracted text and the visual features of the image to identify any anomalies, diseases, or health issues.  
-
 3. **Finding Report**: Document all observed anomalies or signs of disease.  
    - Include any measurements (e.g., triglycerides, HBa1c, HDL) in the format:  
      `{"findings": "Condition only if risky: measurement type -- value with unit(current range)"}`  
    - Simplify the finding in **3 words** at the beginning when helpful.  
-
 4. **Checking for Past**: If a disease is family history or previously recovered, mark severity as:  
    `"severity": "severity of anomaly (Past Anomaly but Still Under Risk)"`
-
 5. **Recommendations and Next Steps**: Provide detailed recommendations (tests, follow-ups, consultations).  
-
 6. **Treatment Suggestions**: Offer preliminary treatments or interventions.  
-
 7. **Output Format**: Always return a JSON object containing both the raw extracted text and the structured analysis, like this:
-
 ```json
 {
   "ocr_text": "<<<FULL VERBATIM TEXT FROM THE PDF/IMAGE>>>",
@@ -127,7 +117,6 @@ Your responsibilities are:
     }
   ]
 }
-
 Important Notes: 
     1. Scope of Response: Only respond if the image pertains to a human health issue. 
     2. Clarity of Image: Ensure the image is clear and suitable for accurate analysis. 
@@ -136,33 +125,24 @@ Important Notes:
     5. Completely UPPERCASE the main concern in the finding """
 
 system_prompt_chat = """
-*** Role: Medical Guidance Facilitator
-*** Objective:
-Analyze medical data, provide concise, evidence-based insights, and recommend actionable next steps for patient care. This includes suggesting local physicians or specialists within a user-specified mile radius, prioritizing in-network options when insurance information is available, and maintaining strict safety compliance with appropriate disclaimers.
-*** Capabilities:
-1. Report Analysis – Review and interpret findings in uploaded medical reports.
-2. Historical Context – Compare current findings with any available previous reports.
-3. Medical Q&A – Answer specific questions about the report using trusted medical sources.
-4. Specialist Matching – Recommend relevant physician specialties for identified conditions.
-5. Local Physician Recommendations – List at least two real physician or clinic options within the user-specified mile radius (include name, specialty, address, distance from user, and contact info) based on the patient’s location and clinical need.
-6. Insurance Guidance – If insurance/network information is provided, prioritize in-network physicians.
-7. Safety Protocols – Include a brief disclaimer encouraging users to verify information, confirm insurance coverage, and consult providers directly.
-*** Response Structure:
-Start with a direct answer to the user’s primary question (maximum 4 concise sentences, each on a new line).
-If a physician/specialist is needed, recommend at least two local providers within the requested radius (include name, specialty, address, distance, and contact info).
-If insurance details are available, indicate which physicians are in-network.
-End with a short safety disclaimer.
-***Input Fields:
-Provided Document Text: {document_text}
+*** Role: Medical Chat Assistant ***
+You are a concise and empathetic medical chatbot. Your job is to give clear, short answers (max 3-4 sentences) based only on the provided medical report text.
+
+Rules:
+- Avoid repeating the entire report; focus only on what is directly relevant to the user’s question.
+- Give top 2 actionable steps if needed.
+- If condition is serious, suggest consulting a doctor immediately.
+- Always end with: "Check with your physician before acting."
+
+Input:
+Report Text: {document_text}
 User Question: {user_question}
-Assistant Answer:
+Response:
 """
 
-# Initialize model
 model = genai.GenerativeModel(model_name="gemini-2.5-flash-lite")
 
 async def _call_model_blocking(request_inputs, generation_cfg, safety_cfg):
-    """Run blocking model call in threadpool (so uvicorn's event loop isn't blocked)."""
     fn = functools.partial(
         model.generate_content,
         request_inputs,
@@ -172,7 +152,6 @@ async def _call_model_blocking(request_inputs, generation_cfg, safety_cfg):
     loop = asyncio.get_event_loop()
     return await loop.run_in_executor(None, fn)
 
-
 async def analyze_image(image_bytes: bytes, mime_type: str, prompt: Optional[str] = None) -> Any:
     base64_img = base64.b64encode(image_bytes).decode("utf-8")
     text_prompt = (prompt or system_prompt).strip()
@@ -206,7 +185,6 @@ async def analyze_image(image_bytes: bytes, mime_type: str, prompt: Optional[str
         match = re.search(r"(\[.*\]|\{.*\})", clean, re.DOTALL)
         if match:
             try:
-                 
                 parsed =  json.loads(match.group(1)), None
                 ocr_text = parsed["ocr_text"]
                 analysis = parsed["analysis"]
@@ -226,16 +204,10 @@ def get_past_reports_from_sqllite(user_id: str):
         history_text = "No past reports found for this user."
     return history_text
 
-
 @app.post("/chat/", response_model=ChatResponse)
 async def chat_endpoint(request: ChatRequest):
     global result
     print(f"Received chat request for user: {request.user_id}")
-    """
-    Chatbot endpoint that answers questions based on the last analyzed document and user history.
-    """
-   
-    #history_text = get_past_reports_from_firestore(request.user_id)
     full_document_text = get_past_reports_from_sqllite(request.user_id.strip())
     
     full_document_text =  EXTRACTED_TEXT_CACHE+"\n\n" + "PAST REPORTS:\n" + full_document_text
@@ -243,7 +215,6 @@ async def chat_endpoint(request: ChatRequest):
     if not full_document_text:
         raise HTTPException(status_code=400, detail="No past reports or current data exists for this user")
     
-    
     try:
         full_prompt = system_prompt_chat.format(
             document_text=full_document_text,
@@ -258,20 +229,13 @@ async def chat_endpoint(request: ChatRequest):
 
 @app.post("/analyze")
 async def analyze_endpoint(file: UploadFile = File(...), prompt: str = Form(None)):
-    
-    """
-    Upload an image file (field name `file`) and optional text `prompt`.
-    Returns parsed JSON (or raw model output if JSON couldn't be parsed).
-    """
-
     global result,EXTRACTED_TEXT_CACHE
     
     filename = file.filename.lower()
     print(f"Received analyze request for file {filename}")
-    contents = await file.read()  # <-- this gets the uploaded file bytes
+    contents = await file.read()
     mime = file.content_type or "image/png"
     
-    #result = await analyze_image(contents, mime, prompt)
     try:
         result, ocr_text = await analyze_image(contents, mime, prompt)
         EXTRACTED_TEXT_CACHE = ocr_text
@@ -301,16 +265,12 @@ def _log_routes():
         if isinstance(r, APIRoute):
             print(" ", r.path, r.methods)
 
-
-
 def main():
-    """Run the application."""
     try:
         logger.info(f"Starting server on 8000")
         logger.info(f"Debug mode: true")
 
         if Config.DEBUG:
-            # Use import string for reload mode
             uvicorn.run(
                 "main:app",
                 host="localhost",
@@ -319,7 +279,6 @@ def main():
                 log_level="debug"
             )
         else:
-            # Use app instance for production
             uvicorn.run(
                 app,
                 host="localhost",
@@ -332,6 +291,5 @@ def main():
         logger.error(f"Failed to start server: {e}")
         raise
 
-
 if __name__ == "__main__":
-    main()
+    main()