Update backend.py

backend.py

@@ -1,4 +1,12 @@
 import os
+from ast import List
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import io
+import fitz
+import traceback
+import pandas as pd
+
 import base64
 import json
 import re
@@ -7,33 +15,56 @@ import functools
 from typing import Any, Optional
 
 import google.generativeai as genai
-from fastapi import FastAPI, UploadFile, File, Form, HTTPException
-from fastapi.responses import JSONResponse
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException, APIRouter, Request
+from fastapi.responses import JSONResponse, RedirectResponse
+from fastapi.staticfiles import StaticFiles
+import firebase_admin
+from firebase_admin import credentials, firestore
+from google.generativeai import generative_models
 from pydantic import BaseModel
+from past_reports import router as reports_router, db_fetch_reports
+
+from api_key import GEMINI_API_KEY 
+
+app = FastAPI()
+api = APIRouter(prefix="/api")
+app.include_router(api)
+
+EXTRACTED_TEXT_CACHE = ""
+
+app.mount("/app", StaticFiles(directory="web", html=True), name="web")
+app.include_router(reports_router)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/")
+def root():
+    return RedirectResponse(url="/app/")
 
 class AnalyzeRequest(BaseModel):
     image_base64: str
-    prompt: str | None = None
+    prompt: Optional[str] = None
 
-API_KEY = None
-try:
-    from api_key import GEMINI_API_KEY as API_KEY   # <-- match the name in api_key.py
-except ImportError:
-    API_KEY = os.getenv("GEMINI_API_KEY")
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", GEMINI_API_KEY)
 
-if not API_KEY:
+if not GEMINI_API_KEY:
     raise RuntimeError(
-        "No Google API key found. Put it in api_key.py as `GEMINI_API_KEY = '...'` or set env var GEMINI_API_KEY."
+        "No Gemini API key found. Put it in api_key.py as `GEMINI_API_KEY = '...'` or set env var GEMINI_API_KEY."
     )
 
-genai.configure(api_key=API_KEY)
+genai.configure(api_key=GEMINI_API_KEY)
 
 generation_config = {
     "temperature": 0.2,    
     "top_p": 0.95,
     "top_k": 40,
     "max_output_tokens": 2048,
-    "response_mime_type": "application/json",
 }
 
 safety_settings = [
@@ -43,21 +74,59 @@ 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
+    
+class ChatResponse(BaseModel):
+    answer: str
+    
+class TextRequest(BaseModel):
+    text: str
+
 system_prompt = """ As a highly skilled medical practitioner specializing in image analysis, you are tasked with examining medical images for a renowned hospital. Your expertise is crucial in identifying any anomalies, diseases, or health issues that may be present in the images. Your responsibilities include: 
 1. Detailed Analysis: Thoroughly analyze each image, focusing on identifying any abnormal findings that may indicate underlying medical conditions. 
-2. Finding Report: Document all observed anomalies or signs of disease. Clearly articulate these findings in a structured report format, ensuring accuracy and clarity. 
-3. Recommendations and Next Steps: Provide detailed recommendations based on your findings. Outline the necessary follow-up actions or additional tests required to confirm diagnoses or assess treatment options. 
-4. Treatment Suggestions: Offer preliminary treatment suggestions or interventions based on the identified conditions, collaborating with the healthcare team to develop comprehensive patient care plans. 
-5. Output Format: Your output should be a JSON array (list) of objects, each describing one disease or medical finding using the structure below: 
+2. Finding Report: Document all observed anomalies or signs of disease. Clearly articulate these findings in a structured report format, ensuring accuracy and clarity. Also include any measurement found such as trygliceride, HBa1c, and hdl levels. When presenting any found measurement, relay it in this format: {"findings": "Condition that this measurement could affect only if the value is in a risky rang: masurement type -- value with unit(current range). For example: {"findings": "Diabetes: Hba1c -- 8%(diabetic)", "ldl -- 80mg/dL(optimal)"}"
+    At the beginning of every finding, if needed try to simplify the information given in 3 words.
+3. Checking for Past: If a disease is considered family history or is a disease that the patient recovered from(anything anomoly that is historical), please mention this in severity using this format: "severity" : "severity of the anomoly (Past Anomoly but Still Under Risk)"
+4. Recommendations and Next Steps: Provide detailed recommendations based on your findings. Outline the necessary follow-up actions or additional tests required to confirm diagnoses or assess treatment options. 
+5. Treatment Suggestions: Offer preliminary treatment suggestions or interventions based on the identified conditions, collaborating with the healthcare team to develop comprehensive patient care plans. 
+6. Output Format: Your output should be a JSON array (list) of objects, each describing one disease or medical finding using the structure below: 
 [{"findings": "Description of the first disease or condition.", "severity": "MILD/SEVERE/CRITICAL", "recommendations": ["Follow-up test 1", "Follow-up test 2"], "treatment_suggestions": ["Treatment 1", "Treatment 2"], "home_care_guidance": ["Care tip 1", "Care tip 2"] }, { "findings": "Description of the second disease or condition.", "severity": "MILD/SEVERE/CRITICAL", "recommendations": ["Follow-up test A", "Follow-up test B"], "treatment_suggestions": ["Treatment A", "Treatment B"], "home_care_guidance": ["Care tip A", "Care tip B"] } ] 
-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. 3. Disclaimer: Accompany your analysis with the disclaimer: “Consult with a doctor before making any decisions.” 4. Your Insights are Invaluable: Your insights play a crucial role in guiding clinical decisions. Please proceed with your analysis, adhering to the structured approach outlined above. """
+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. 
+    3. As an AI, don't produce any output than doesn't follow this format and also remember that we need your input on the medical report for a team of renowned doctors to recheck your readings and determine AI's role in the medical industry. 
+    4. Your Insights are Invaluable: Your insights play a crucial role in guiding clinical decisions. Please proceed with your analysis, adhering to the structured approach outlined above.
+    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}
+User Question: {user_question}
+Assistant Answer:
+"""
 
 # Initialize model
 model = genai.GenerativeModel(model_name="gemini-2.5-flash-lite")
 
-app = FastAPI()
-
-
 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(
@@ -74,7 +143,6 @@ async def analyze_image(image_bytes: bytes, mime_type: str, prompt: Optional[str
     base64_img = base64.b64encode(image_bytes).decode("utf-8")
     text_prompt = (prompt or system_prompt).strip()
 
-    # prepare request — two messages: image inline + text prompt
     request_inputs = [
         {"inline_data": {"mime_type": mime_type, "data": base64_img}},
         {"text": text_prompt},
@@ -85,20 +153,16 @@ async def analyze_image(image_bytes: bytes, mime_type: str, prompt: Optional[str
     except Exception as e:
         raise RuntimeError(f"Model call failed: {e}")
 
-    # Try to extract textual content robustly
     text = getattr(response, "text", None)
     if not text and isinstance(response, dict):
-        # older or alternative shapes
         candidates = response.get("candidates") or []
         if candidates:
             text = candidates[0].get("content") or candidates[0].get("text")
     if not text:
         text = str(response)
 
-    # remove triple-backtick fences and stray code hints
     clean = re.sub(r"```(?:json)?", "", text).strip()
 
-    # Try to parse JSON. If strict parse fails, try to extract first JSON-like block.
     try:
         parsed = json.loads(clean)
         return parsed
@@ -111,9 +175,34 @@ async def analyze_image(image_bytes: bytes, mime_type: str, prompt: Optional[str
                 return {"raw_found_json": match.group(1)}
         return {"raw_output": clean}
 
+def get_past_reports_from_sqllite(user_id: str):
+    try:
+        reports = db_fetch_reports(user_id=user_id, limit=10, offset=0)
+        
+        history_text = ""
+        for report in reports:
+            history_text += f"Report from {report.get('report_date', 'N/A')}:\n{report.get('ocr_text', 'No OCR text found')}\n\n"
+    except Exception as e:
+        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
+    try:
+        document_text = json.dumps(result) if result else "No parsed text available"
+        full_prompt = system_prompt_chat.format(
+            document_text=document_text,
+            user_question=request.question
+        )
+        response = model.generate_content(full_prompt)
+        return ChatResponse(answer=response.text)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Chat error: {e}")
 
 @app.post("/analyze")
 async def analyze_endpoint(file: UploadFile = File(...), prompt: str = Form(None)):
+    global result
     """
     Upload an image file (field name `file`) and optional text `prompt`.
     Returns parsed JSON (or raw model output if JSON couldn't be parsed).
@@ -134,3 +223,14 @@ async def analyze_json(req: AnalyzeRequest):
     result = await analyze_image(image_bytes, "image/png", req.prompt)
     return {"result": result}
    
+@app.get("/health/")
+def health():
+    return {"response": "ok"}
+
+@app.on_event("startup")
+def _log_routes():
+    from fastapi.routing import APIRoute
+    print("Mounted routes:")
+    for r in app.routes:
+        if isinstance(r, APIRoute):
+            print(" ", r.path, r.methods)