backend.py

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
import asyncio
import functools
from typing import Any, Optional

import google.generativeai as genai
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: Optional[str] = None

GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", GEMINI_API_KEY)

if not GEMINI_API_KEY:
    raise RuntimeError(
        "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=GEMINI_API_KEY)

generation_config = {
    "temperature": 0.2,    
    "top_p": 0.95,
    "top_k": 40,
    "max_output_tokens": 2048,
}

safety_settings = [
    {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
    {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
    {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
    {"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. 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. 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")

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,
        generation_config=generation_cfg,
        safety_settings=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()

    request_inputs = [
        {"inline_data": {"mime_type": mime_type, "data": base64_img}},
        {"text": text_prompt},
    ]

    try:
        response = await _call_model_blocking(request_inputs, generation_config, safety_settings)
    except Exception as e:
        raise RuntimeError(f"Model call failed: {e}")

    text = getattr(response, "text", None)
    if not text and isinstance(response, dict):
        candidates = response.get("candidates") or []
        if candidates:
            text = candidates[0].get("content") or candidates[0].get("text")
    if not text:
        text = str(response)

    clean = re.sub(r"```(?:json)?", "", text).strip()

    try:
        parsed = json.loads(clean)
        return parsed
    except json.JSONDecodeError:
        match = re.search(r"(\[.*\]|\{.*\})", clean, re.DOTALL)
        if match:
            try:
                return json.loads(match.group(1))
            except json.JSONDecodeError:
                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
    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)

    #full_document_text =  +"\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:
        document_text = json.dumps(full_document_text) 
        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)):
    
    """
    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
    contents = await file.read()  # <-- this gets the uploaded file bytes
    mime = file.content_type or "image/png"
    result = await analyze_image(contents, mime, prompt)
    try:
        result = await analyze_image(contents, mime, prompt)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
    return JSONResponse(content={"Detected_Anomolies": result})

@app.post("/analyze_json")
async def analyze_json(req: AnalyzeRequest):
    import base64
    image_bytes = base64.b64decode(req.image_base64)
    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)