app.py

import sys
import os
import json
import shutil
import re
import gc
import time
from datetime import datetime
from typing import List, Tuple, Dict, Union, Optional
from fastapi import FastAPI, UploadFile, File, HTTPException
from fastapi.responses import FileResponse, JSONResponse
from fastapi.middleware.cors import CORSMiddleware
import pandas as pd
import pdfplumber
import torch
import matplotlib.pyplot as plt
from fpdf import FPDF
import unicodedata
import uvicorn

# === Configuration ===
persistent_dir = "/data/hf_cache"
model_cache_dir = os.path.join(persistent_dir, "txagent_models")
tool_cache_dir = os.path.join(persistent_dir, "tool_cache")
file_cache_dir = os.path.join(persistent_dir, "cache")
report_dir = os.path.join(persistent_dir, "reports")

# Create directories if they don't exist
for d in [model_cache_dir, tool_cache_dir, file_cache_dir, report_dir]:
    os.makedirs(d, exist_ok=True)

# Set environment variables
os.environ["HF_HOME"] = model_cache_dir
os.environ["TRANSFORMERS_CACHE"] = model_cache_dir
os.environ["MPLCONFIGDIR"] = "/tmp/matplotlib"  # Fix for matplotlib permission issues

# Set up Python path
current_dir = os.path.dirname(os.path.abspath(__file__))
src_path = os.path.abspath(os.path.join(current_dir, "src"))
sys.path.insert(0, src_path)

# Import TxAgent after setting up paths
from txagent.txagent import TxAgent

# Constants
MAX_MODEL_TOKENS = 131072
MAX_NEW_TOKENS = 4096
MAX_CHUNK_TOKENS = 8192
BATCH_SIZE = 1
PROMPT_OVERHEAD = 300
SAFE_SLEEP = 0.5

# Initialize FastAPI app
app = FastAPI(
    title="Clinical Patient Support System API",
    description="API for analyzing and summarizing unstructured medical files",
    version="1.0.0"
)

# CORS configuration
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize agent at startup
agent = None

@app.on_event("startup")
async def startup_event():
    global agent
    try:
        agent = init_agent()
    except Exception as e:
        raise RuntimeError(f"Failed to initialize agent: {str(e)}")

def init_agent() -> TxAgent:
    """Initialize and return the TxAgent instance."""
    tool_path = os.path.join(tool_cache_dir, "new_tool.json")
    if not os.path.exists(tool_path):
        shutil.copy(os.path.abspath("data/new_tool.json"), tool_path)
    
    agent = TxAgent(
        model_name="mims-harvard/TxAgent-T1-Llama-3.1-8B",
        rag_model_name="mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B",
        tool_files_dict={"new_tool": tool_path},
        force_finish=True,
        enable_checker=True,
        step_rag_num=4,
        seed=100
    )
    agent.init_model()
    return agent

# Utility functions (keep your existing functions but add error handling)
def estimate_tokens(text: str) -> int:
    """Estimate the number of tokens in the given text."""
    return len(text) // 4 + 1

def clean_response(text: str) -> str:
    """Clean and format the response text."""
    if not text:
        return ""
    text = re.sub(r"\[.*?\]|\bNone\b", "", text, flags=re.DOTALL)
    text = re.sub(r"\n{3,}", "\n\n", text)
    return text.strip()

def extract_text_from_excel(path: str) -> str:
    """Extract text from Excel file."""
    try:
        all_text = []
        xls = pd.ExcelFile(path)
        for sheet_name in xls.sheet_names:
            try:
                df = xls.parse(sheet_name).astype(str).fillna("")
            except Exception:
                continue
            for _, row in df.iterrows():
                non_empty = [cell.strip() for cell in row if cell.strip()]
                if len(non_empty) >= 2:
                    text_line = " | ".join(non_empty)
                    if len(text_line) > 15:
                        all_text.append(f"[{sheet_name}] {text_line}")
        return "\n".join(all_text)
    except Exception as e:
        raise RuntimeError(f"Failed to extract text from Excel: {str(e)}")

def extract_text(file_path: str) -> str:
    """Extract text from supported file types."""
    try:
        if file_path.endswith(".xlsx"):
            return extract_text_from_excel(file_path)
        elif file_path.endswith(".csv"):
            df = pd.read_csv(file_path).astype(str).fillna("")
            return "\n".join(
                " | ".join(cell.strip() for cell in row if cell.strip())
                for _, row in df.iterrows()
                if len([cell for cell in row if cell.strip()]) >= 2
            )
        elif file_path.endswith(".pdf"):
            with pdfplumber.open(file_path) as pdf:
                return "\n".join(page.extract_text() or "" for page in pdf.pages)
        else:
            return ""
    except Exception as e:
        raise RuntimeError(f"Failed to extract text from file: {str(e)}")

# API endpoints
@app.post("/analyze")
async def analyze_document(file: UploadFile = File(...)):
    """Analyze a medical document and return results."""
    start_time = time.time()
    
    try:
        # Save the uploaded file temporarily
        temp_path = os.path.join(file_cache_dir, file.filename)
        with open(temp_path, "wb") as f:
            f.write(await file.read())
        
        extracted = extract_text(temp_path)
        if not extracted:
            raise HTTPException(status_code=400, detail="Could not extract text from the file")
        
        chunks = split_text(extracted)
        batches = batch_chunks(chunks, batch_size=BATCH_SIZE)
        batch_results = analyze_batches(agent, batches)
        
        valid_results = [res for res in batch_results if not res.startswith("❌")]
        if not valid_results:
            raise HTTPException(status_code=400, detail="No valid analysis results were generated")

        final_summary = generate_final_summary(agent, "\n\n".join(valid_results))
        
        # Generate report files
        report_filename = f"report_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
        report_path = os.path.join(report_dir, f"{report_filename}.md")
        with open(report_path, 'w', encoding='utf-8') as f:
            f.write(f"# Final Medical Report\n\n{final_summary}")

        pdf_path = generate_pdf_report_with_charts(final_summary, report_path, detailed_batches=batch_results)
        
        # Clean up temp file
        os.remove(temp_path)
        
        return JSONResponse({
            "status": "success",
            "summary": final_summary,
            "report_path": f"/reports/{os.path.basename(pdf_path)}",
            "processing_time": f"{time.time() - start_time:.2f} seconds",
            "detailed_outputs": batch_results
        })
        
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/reports/{filename}")
async def download_report(filename: str):
    """Download a generated report."""
    file_path = os.path.join(report_dir, filename)
    if not os.path.exists(file_path):
        raise HTTPException(status_code=404, detail="Report not found")
    return FileResponse(file_path, media_type='application/pdf', filename=filename)

@app.get("/status")
async def service_status():
    """Check service status."""
    return {
        "status": "running",
        "version": "1.0.0",
        "model": "mims-harvard/TxAgent-T1-Llama-3.1-8B",
        "max_tokens": MAX_MODEL_TOKENS,
        "supported_file_types": [".pdf", ".xlsx", ".csv"]
    }

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)