Update backend.py

backend.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, UploadFile, File, Form, HTTPException
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException,APIRouter
 from fastapi.middleware.cors import CORSMiddleware
 from pydantic import BaseModel
 from typing import Optional
@@ -12,6 +12,13 @@ import re
 import os
 import google.generativeai as genai
 from dotenv import load_dotenv
+from fastapi.responses import RedirectResponse
+from fastapi.staticfiles import StaticFiles
+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
+
 
 load_dotenv() 
 def extract_non_negated_keywords(text: str) -> list:
@@ -27,6 +34,25 @@ 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.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -35,8 +61,18 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+@app.get("/")
+def root():
+    return RedirectResponse(url="/app/")
+    
 EXTRACTED_TEXT_CACHE: str = ""
 
+
+df = pd.read_csv("measurement.csv")
+df.columns = df.columns.str.lower()
+df['measurement'] = df['measurement'].str.lower()
+
+
 try:
     gemini_api_key = os.environ.get("GEMINI_API_KEY")
     if not gemini_api_key:
@@ -114,56 +150,6 @@ def ocr_text_from_image(image_bytes: bytes) -> str:
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"OCR error: {e}")
 
-@app.post("/analyze/")
-async def analyze(
-    file: UploadFile = File(...),
-    model: Optional[str] = Form("bert")
-):
-    global EXTRACTED_TEXT_CACHE
-    if not file.filename:
-        raise HTTPException(status_code=400, detail="No file uploaded.")
-    
-    filename = file.filename.lower()
-    ocr_full = ""
-    
-    try:
-        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"
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"File processing error: {e}")
-
-    EXTRACTED_TEXT_CACHE = ocr_full.strip()
-
-    found_diseases = extract_non_negated_keywords(EXTRACTED_TEXT_CACHE)
-    resolutions = []
-    for disease in found_diseases:
-        severity, _ = classify_disease_and_severity(EXTRACTED_TEXT_CACHE)
-        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(), [])
-
-        resolutions.append({
-            "findings": disease,
-            "severity": severity,
-            "recommendations": next_steps,
-            "treatment_suggestions": f"Consult a specialist: {specialist}",
-            "home_care_guidance": home_care,
-            "info_link": link
-        })
-    
-    return {
-        "ocr_text": EXTRACTED_TEXT_CACHE,
-        "resolutions": resolutions
-    }
 
 @app.post("/chat/", response_model=ChatResponse)
 async def chat_endpoint(request: ChatRequest):
@@ -188,28 +174,7 @@ async def chat_endpoint(request: ChatRequest):
         print(f"Gemini API error: {traceback.format_exc()}")
         raise HTTPException(status_code=500, detail=f"An error occurred during chat response generation: {e}")
 
-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
 
-df = pd.read_csv("measurement.csv")
-df.columns = df.columns.str.lower()
-df['measurement'] = df['measurement'].str.lower()
-
-app = FastAPI()
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=[
-        "http://localhost:8002"
-        "http://localhost:9000"
-        "http://localhost:5501"
-    ],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
 
 
 def extract_images_from_pdf_bytes(pdf_bytes: bytes) -> list:
@@ -329,6 +294,7 @@ def analyze_text(text):
 def health():
     return {"response": "ok"}
 
+
 @app.on_event("startup")
 def _log_routes():
     from fastapi.routing import APIRoute