Update backend.py

backend.py

@@ -24,7 +24,8 @@ from bert import analyze_with_clinicalBert, classify_disease_and_severity, extra
 from disease_links import diseases as disease_links
 from disease_steps import disease_next_steps
 from disease_support import disease_doctor_specialty, disease_home_care
-from past_reports import router as reports_router, db_fetch_reports
+import datetime
+from typing import Optional, List 
 
 model = genai.GenerativeModel('gemini-1.5-flash')
 df = pd.read_csv("measurement.csv")
@@ -42,21 +43,7 @@ api = APIRouter(prefix="/api")
 app.include_router(api)
 
 
-'''app.add_middleware(
-    CORSMiddleware,
-    allow_origins=[
-        "http://localhost:8002"
-        "http://localhost:9000"
-        "http://localhost:5501"
-    ],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)'''
-
-
 app.mount("/app", StaticFiles(directory="web", html=True), name="web")
-app.include_router(reports_router)
 
 app.add_middleware(
     CORSMiddleware,
@@ -95,13 +82,15 @@ try:
 except Exception as e:
     raise RuntimeError(f"Failed to configure Firebase: {e}")
 
-class ChatRequest(BaseModel):
-    user_id: Optional[str] = "anonymous"
-    question: str
 
 class ChatResponse(BaseModel):
     answer: str
 
+
+class ChatRequest(BaseModel):
+    question: str
+    user_id: Optional[str] = "anonymous"
+
 class ReportData(BaseModel):
     user_id: str
     reportDate: Optional[str] = None
@@ -169,7 +158,16 @@ def ocr_text_from_image(image_bytes: bytes) -> str:
     print(response_text)
 
     return response_text
-def get_past_reports_from_firestore(user_id: str):
+
+@app.post("/chat/", response_model=ChatResponse)
+async def chat_endpoint(request: ChatRequest):
+    """
+    Chatbot endpoint that answers questions based on the last analyzed document and user history.
+    """
+    global EXTRACTED_TEXT_CACHE
+    if not EXTRACTED_TEXT_CACHE:
+        raise HTTPException(status_code=400, detail="Please provide a document context by analyzing text first.")
+    
     try:
         reports_ref = db.collection('users').document(request.user_id).collection('reports')
         docs = reports_ref.order_by('timestamp', direction=firestore.Query.DESCENDING).limit(10).stream()
@@ -180,35 +178,8 @@ def get_past_reports_from_firestore(user_id: str):
             history_text += f"Report from {report_data.get('timestamp', 'N/A')}:\n{report_data.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
-
-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):
-    """
-    Chatbot endpoint that answers questions based on the last analyzed document and user history.
-    """
-    print("Received chat request for user:", request.user_id)
-    #history_text = get_past_reports_from_firestore(request.user_id)
-    history_text = get_past_reports_from_sqllite(request.user_id)
-
-    full_document_text = EXTRACTED_TEXT_CACHE + "\n\n" + "PAST REPORTS:\n" + history_text
-
-    if not full_document_text:
-        raise HTTPException(status_code=400, detail="No past reports or current data exists for this user")
-    
     
-   
+    full_document_text = EXTRACTED_TEXT_CACHE + "\n\n" + "PAST REPORTS:\n" + history_text
     
     try:
         full_prompt = system_prompt_chat.format(
@@ -234,7 +205,6 @@ async def analyze(
     filename = file.filename.lower()
     detected_diseases = set()
     ocr_full = ""
-    print("Received request for file:", filename)
     if filename.endswith(".pdf"):
         pdf_bytes = await file.read()
         image_bytes_list = extract_images_from_pdf_bytes(pdf_bytes)
@@ -255,19 +225,22 @@ async def analyze(
         return {"message": "Gemini model not available; please use BERT model."}
 
     found_diseases = extract_non_negated_keywords(ocr_full)
+    print(f"CALLING FOUND DISEASES: {found_diseases}")
     past = detect_past_diseases(ocr_full)
+    print(f"CALLING PAST DISEASES: {past}")
 
     for disease in found_diseases:
         if disease in past:    
             severity = classify_disease_and_severity(disease)
             detected_diseases.add(((f"{disease}(detected as historical condition, but still under risk.)"), severity))
+            print(f"DETECTED DISEASES(PAST): {detected_diseases}")
         else:
             severity = classify_disease_and_severity(disease)
             detected_diseases.add((disease, severity))
-           
+            print(f"DETECTED DISEASES: {detected_diseases}")
         
-   
-    print("Detected diseases:", detected_diseases)
+    print("OCR TEXT:", ocr_text)
+    print("Detected diseases:", found_diseases)
     ranges = analyze_measurements(ocr_full, df)
 
 
@@ -301,6 +274,7 @@ async def analyze(
         next_steps_range = disease_next_steps.get(condition.lower(), ['Consult a doctor'])
         specialist_range = disease_doctor_specialty.get(condition.lower(), "General Practitioner")
         home_care_range = disease_home_care.get(condition.lower(), [])
+        print(f"HELLO!: {measurement}")
                     
         condition_version = condition.upper()    
         severity_version = severity.upper()
@@ -314,11 +288,12 @@ async def analyze(
             "info_link": link_range
         })
                             
-    
+    print(ocr_full)
     ranges = analyze_measurements(ocr_full, df)
     print(analyze_measurements(ocr_full, df))
     # print ("Ranges is being printed", ranges)
     historical_med_data = detect_past_diseases(ocr_full)
+    print("***End of Code***")
     
     return {
         "ocr_text": ocr_full.strip(),