Update backend.py (#26)

- Update backend.py (d80f50afe861388c20d603b4ce22988428b9f907)


Co-authored-by: Ayush Sankar <AyushSankar13@users.noreply.huggingface.co>

backend.py

@@ -32,29 +32,11 @@ df = pd.read_csv("measurement.csv")
 df.columns = df.columns.str.lower()
 df['measurement'] = df['measurement'].str.lower()
 
-disease_links = {"cholesterol": "https://www.webmd.com/cholesterol"}
-disease_next_steps = {"cholesterol": ["Consult a doctor for a lipid panel."]}
-disease_doctor_specialty = {"cholesterol": "Cardiologist"}
-disease_home_care = {"cholesterol": ["Maintain a healthy diet."]}
-
 app = FastAPI()
-
 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=["*"],
-)'''
-
+EXTRACTED_TEXT_CACHE = ""
 
 app.mount("/app", StaticFiles(directory="web", html=True), name="web")
 app.include_router(reports_router)
@@ -70,8 +52,6 @@ app.add_middleware(
 @app.get("/")
 def root():
     return RedirectResponse(url="/app/")
-    
-EXTRACTED_TEXT_CACHE: str = ""
 
 try:
     gemini_api_key = os.environ.get("GEMINI_API_KEY", GEMINI_API_KEY)
@@ -96,13 +76,19 @@ try:
 except Exception as e:
     raise RuntimeError(f"Failed to configure Firebase: {e}")
 
+
+# --- 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 for Gemini ---
 system_prompt_chat = """
 *** Role: Medical Guidance Facilitator
 *** Objective:
@@ -135,7 +121,6 @@ def extract_images_from_pdf_bytes(pdf_bytes: bytes) -> list:
         images.append(buf.getvalue())
     return images
 
-
 def clean_ocr_text(text: str) -> str:
     text = text.replace("\x0c", " ")
     text = text.replace("\u00a0", " ")    
@@ -143,6 +128,7 @@ def clean_ocr_text(text: str) -> str:
     text = re.sub(r'\s+', ' ', text)      
     return text.strip()
 
+
 def ocr_text_from_image(image_bytes: bytes) -> str:
     base64_image = base64.b64encode(image_bytes).decode('utf-8')
 
@@ -161,18 +147,6 @@ def ocr_text_from_image(image_bytes: bytes) -> str:
     print(response_text)
 
     return response_text
-def get_past_reports_from_firestore(user_id: str):
-    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()
-        
-        history_text = ""
-        for doc in docs:
-            report_data = doc.to_dict()
-            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:
@@ -185,34 +159,6 @@ 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):
-    """
-    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")
-    
-    
-   
-    
-    try:
-        full_prompt = system_prompt_chat.format(
-            document_text=full_document_text,
-            user_question=request.question
-        )
-        response = model.generate_content(full_prompt)
-        return ChatResponse(answer=response.text)
-    except Exception as e:
-        print(f"Gemini API error: {traceback.format_exc()}")
-        raise HTTPException(status_code=500, detail=f"An error occurred during chat response generation: {e}")
-
 @app.post("/analyze/")
 async def analyze(
     file: UploadFile = File(...),
@@ -226,7 +172,6 @@ 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)
@@ -247,19 +192,22 @@ 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)
 
 
@@ -293,6 +241,7 @@ 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()
@@ -306,11 +255,12 @@ 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(),
@@ -319,7 +269,7 @@ async def analyze(
 
 class TextRequest(BaseModel):
     text: str
-    
+
 @app.post("/analyze-text")
 async def analyze_text_endpoint(request: TextRequest):
     try:
@@ -327,7 +277,7 @@ async def analyze_text_endpoint(request: TextRequest):
     except Exception as e:
         print("ERROR in /analyze-text:", traceback.format_exc())
         raise HTTPException(status_code=500, detail=f"Error analyzing text: {str(e)}")
-    
+
 
 def analyze_text(text):
     severity, disease = classify_disease_and_severity(text)
@@ -335,8 +285,40 @@ def analyze_text(text):
         "extracted_text": text,
         "summary": f"Detected Disease: {disease}, Severity: {severity}"
     }
-
-
+    
+@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()
+        
+        history_text = ""
+        for doc in docs:
+            report_data = doc.to_dict()
+            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."
+    
+    full_document_text = EXTRACTED_TEXT_CACHE + "\n\n" + "PAST REPORTS:\n" + history_text
+    
+    try:
+        full_prompt = system_prompt_chat.format(
+            document_text=full_document_text,
+            user_question=request.question
+        )
+        response = model.generate_content(full_prompt)
+        return ChatResponse(answer=response.text)
+    except Exception as e:
+        print(f"Gemini API error: {traceback.format_exc()}")
+        raise HTTPException(status_code=500, detail=f"An error occurred during chat response generation: {e}")
+    
 @app.get("/health/")
 def health():
     return {"response": "ok"}
@@ -348,3 +330,354 @@ def _log_routes():
     for r in app.routes:
         if isinstance(r, APIRoute):
             print(" ", r.path, r.methods)
+            
+# from ast import List
+# from fastapi import FastAPI, UploadFile, File, Form, HTTPException,APIRouter, Request
+# from fastapi.middleware.cors import CORSMiddleware
+# from pydantic import BaseModel
+# from typing import Optional
+# import pytesseract
+# from PIL import Image
+# import io
+# import fitz  
+# import base64
+# import traceback
+# import pandas as pd
+# import re
+# import os
+# import google.generativeai as genai
+# from dotenv import load_dotenv
+# from fastapi.responses import RedirectResponse
+# from fastapi.staticfiles import StaticFiles
+# import firebase_admin
+# from firebase_admin import credentials, firestore
+# from google.generativeai import generative_models
+
+# from api_key import GEMINI_API_KEY
+# from bert import analyze_with_clinicalBert, classify_disease_and_severity, extract_non_negated_keywords, analyze_measurements, detect_past_diseases
+# 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
+
+# model = genai.GenerativeModel('gemini-1.5-flash')
+# df = pd.read_csv("measurement.csv")
+# df.columns = df.columns.str.lower()
+# df['measurement'] = df['measurement'].str.lower()
+
+# disease_links = {"cholesterol": "https://www.webmd.com/cholesterol"}
+# disease_next_steps = {"cholesterol": ["Consult a doctor for a lipid panel."]}
+# disease_doctor_specialty = {"cholesterol": "Cardiologist"}
+# disease_home_care = {"cholesterol": ["Maintain a healthy diet."]}
+
+# app = FastAPI()
+
+# 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,
+#     allow_origins=["*"],
+#     allow_credentials=True,
+#     allow_methods=["*"],
+#     allow_headers=["*"],
+# )
+
+# @app.get("/")
+# def root():
+#     return RedirectResponse(url="/app/")
+    
+# EXTRACTED_TEXT_CACHE: str = ""
+
+# try:
+#     gemini_api_key = os.environ.get("GEMINI_API_KEY", GEMINI_API_KEY)
+#     if not gemini_api_key:
+#         raise ValueError("No Gemini API key found in environment or api_key.py")
+#     genai.configure(api_key=gemini_api_key)
+# except Exception as e:
+#     raise RuntimeError(f"Failed to configure Gemini API: {e}")
+
+# try:
+#     cred_path = os.environ.get("FIREBASE_SERVICE_ACCOUNT_KEY_PATH", "firebase_key.json")
+    
+#     if not os.path.exists(cred_path):
+#         raise ValueError(
+#             f"Firebase service account key not found. Looked for: {cred_path}. "
+#             "Set FIREBASE_SERVICE_ACCOUNT_KEY_PATH or place firebase_key.json in project root."
+#         )
+
+#     cred = credentials.Certificate(cred_path)
+#     firebase_admin.initialize_app(cred)
+#     db = firestore.client()
+# 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
+
+# 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. 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:
+# """
+
+# def extract_images_from_pdf_bytes(pdf_bytes: bytes) -> list:
+#     print("***Start of Code***")
+#     doc = fitz.open(stream=pdf_bytes, filetype="pdf")
+#     images = []
+#     for page in doc:
+#         pix = page.get_pixmap()
+#         buf = io.BytesIO()
+#         buf.write(pix.tobytes("png"))
+#         images.append(buf.getvalue())
+#     return images
+
+
+# def clean_ocr_text(text: str) -> str:
+#     text = text.replace("\x0c", " ")
+#     text = text.replace("\u00a0", " ")    
+#     text = re.sub(r'(\d)\s*\.\s*(\d)', r'\1.\2', text) 
+#     text = re.sub(r'\s+', ' ', text)      
+#     return text.strip()
+
+# def ocr_text_from_image(image_bytes: bytes) -> str:
+#     base64_image = base64.b64encode(image_bytes).decode('utf-8')
+
+#     image_content = {
+#         'mime_type': 'image/jpeg',
+#         'data': base64_image
+#     }
+
+#     prompt = "Could you read this document and just take all the text that is in it and just paste it back to me in text format. Open and read this document:"
+
+#     response = model.generate_content(
+#         [prompt, image_content]
+#     )
+
+#     response_text = response.text
+#     print(response_text)
+
+#     return response_text
+# def get_past_reports_from_firestore(user_id: str):
+#     try:
+#         reports_ref = db.collection('users').document(user_id).collection('reports')
+#         docs = reports_ref.order_by('timestamp', direction=firestore.Query.DESCENDING).limit(10).stream()
+        
+#         history_text = ""
+#         for doc in docs:
+#             report_data = doc.to_dict()
+#             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")
+    
+    
+   
+    
+#     try:
+#         full_prompt = system_prompt_chat.format(
+#             document_text=full_document_text,
+#             user_question=request.question
+#         )
+#         response = model.generate_content(full_prompt)
+#         return ChatResponse(answer=response.text)
+#     except Exception as e:
+#         print(f"Gemini API error: {traceback.format_exc()}")
+#         raise HTTPException(status_code=500, detail=f"An error occurred during chat response generation: {e}")
+
+# @app.post("/analyze/")
+# async def analyze(
+#     file: UploadFile = File(...),
+#     model: Optional[str] = Form("bert"),
+#     mode: Optional[str] = Form(None)
+# ):
+#     global resolution, EXTRACTED_TEXT_CACHE
+#     if not file.filename:
+#         raise HTTPException(status_code=400, detail="No file uploaded.")
+
+#     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)
+#     else:
+#         content = await file.read()
+#         image_bytes_list = [content]
+
+#     for img_bytes in image_bytes_list:
+#         ocr_text = ocr_text_from_image(img_bytes)
+#         ocr_full += ocr_text + "\n\n"
+#         ocr_full = clean_ocr_text(ocr_full)
+#         print(f"CALLING OCR FULL: {ocr_full}")
+    
+#     EXTRACTED_TEXT_CACHE = ocr_full
+
+
+#     if model.lower() == "gemini":
+#         return {"message": "Gemini model not available; please use BERT model."}
+
+#     found_diseases = extract_non_negated_keywords(ocr_full)
+#     past = detect_past_diseases(ocr_full)
+
+#     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))
+#         else:
+#             severity = classify_disease_and_severity(disease)
+#             detected_diseases.add((disease, severity))
+           
+        
+   
+#     print("Detected diseases:", detected_diseases)
+#     ranges = analyze_measurements(ocr_full, df)
+
+
+#     resolution = []
+#     detected_ranges = []
+#     for disease, severity in detected_diseases:
+#         link = disease_links.get(disease.lower(), "https://www.webmd.com/")
+#         next_steps = disease_next_steps.get(disease.lower(), ["Consult a doctor."])
+#         specialist = disease_doctor_specialty.get(disease.lower(), "General Practitioner")
+#         home_care = disease_home_care.get(disease.lower(), [])
+
+#         resolution.append({
+#             "findings": disease.upper(),
+#             "severity": severity,
+#             "recommendations": next_steps,
+#             "treatment_suggestions": f"Consult a specialist: {specialist}",
+#             "home_care_guidance": home_care,
+#             "info_link": link
+                      
+#     })
+    
+#     for i in ranges:
+#         condition = i[0]
+#         measurement = i[1]
+#         unit = i[2]
+#         severity = i[3]
+#         value = i[4]
+#         range_value = i[5]   # renamed to avoid overwriting Python's built-in "range"
+                    
+#         link_range = disease_links.get(condition.lower(), "https://www.webmd.com/")
+#         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(), [])
+                    
+#         condition_version = condition.upper()    
+#         severity_version = severity.upper()
+                
+#         resolution.append({
+#             "findings": f"{condition_version} -- {measurement}",
+#             "severity": f"{value} {unit} - {severity_version}",
+#             "recommendations": next_steps_range,
+#             "treatment_suggestions": f"Consult a specialist: {specialist_range}",
+#             "home_care_guidance": home_care_range,
+#             "info_link": link_range
+#         })
+                            
+    
+#     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)
+    
+#     return {
+#         "ocr_text": ocr_full.strip(),
+#         "Detected_Anomolies": resolution,
+#     }
+
+# class TextRequest(BaseModel):
+#     text: str
+    
+# @app.post("/analyze-text")
+# async def analyze_text_endpoint(request: TextRequest):
+#     try:
+#         return analyze_text(request.text)
+#     except Exception as e:
+#         print("ERROR in /analyze-text:", traceback.format_exc())
+#         raise HTTPException(status_code=500, detail=f"Error analyzing text: {str(e)}")
+    
+
+# def analyze_text(text):
+#     severity, disease = classify_disease_and_severity(text)
+#     return {
+#         "extracted_text": text,
+#         "summary": f"Detected Disease: {disease}, Severity: {severity}"
+#     }
+
+
+# @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)