app.py

import sys
import os
import pandas as pd
import json
import gradio as gr
from typing import List, Tuple, Dict, Any, Union
import hashlib
import shutil
import re
from datetime import datetime
import time

# Configuration and setup
persistent_dir = "/data/hf_cache"
os.makedirs(persistent_dir, exist_ok=True)

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")

for directory in [model_cache_dir, tool_cache_dir, file_cache_dir, report_dir]:
    os.makedirs(directory, exist_ok=True)

os.environ["HF_HOME"] = model_cache_dir
os.environ["TRANSFORMERS_CACHE"] = model_cache_dir

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)

from txagent.txagent import TxAgent

# Constants
MAX_TOKENS = 32768
MAX_NEW_TOKENS = 2048

def clean_response(text: str) -> str:
    try:
        text = text.encode('utf-8', 'surrogatepass').decode('utf-8')
    except UnicodeError:
        text = text.encode('utf-8', 'replace').decode('utf-8')
    text = re.sub(r"\[.*?\]|\bNone\b", "", text, flags=re.DOTALL)
    text = re.sub(r"\n{3,}", "\n\n", text)
    text = re.sub(r"[^\n#\-\*\w\s\.,:\(\)]+", "", text)
    return text.strip()

def estimate_tokens(text: str) -> int:
    return len(text) // 3.5

def extract_text_from_excel(file_path: str) -> str:
    all_text = []
    xls = pd.ExcelFile(file_path)
    for sheet_name in xls.sheet_names:
        df = xls.parse(sheet_name)
        df = df.astype(str).fillna("")
        rows = df.apply(lambda row: " | ".join(row), axis=1)
        sheet_text = [f"[{sheet_name}] {line}" for line in rows]
        all_text.extend(sheet_text)
    return "\n".join(all_text)

def split_text_into_chunks(text: str, max_tokens: int = MAX_TOKENS) -> List[str]:
    lines = text.split("\n")
    chunks = []
    current_chunk = []
    current_tokens = 0

    for line in lines:
        tokens = estimate_tokens(line)
        if current_tokens + tokens > max_tokens:
            chunks.append("\n".join(current_chunk))
            current_chunk = [line]
            current_tokens = tokens
        else:
            current_chunk.append(line)
            current_tokens += tokens

    if current_chunk:
        chunks.append("\n".join(current_chunk))
    return chunks

def build_prompt_from_text(chunk: str) -> str:
    return f"""
### Unstructured Clinical Records

You are reviewing unstructured, mixed-format clinical documentation from various forms, tables, and sheets.

**Objective:** Identify patterns, missed diagnoses, inconsistencies, and follow-up gaps.

Here is the extracted content chunk:

{chunk}

Please analyze the above and provide:
- Diagnostic Patterns
- Medication Issues
- Missed Opportunities
- Inconsistencies
- Follow-up Recommendations
"""

def init_agent():
    default_tool_path = os.path.abspath("data/new_tool.json")
    target_tool_path = os.path.join(tool_cache_dir, "new_tool.json")

    if not os.path.exists(target_tool_path):
        shutil.copy(default_tool_path, target_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": target_tool_path},
        force_finish=True,
        enable_checker=True,
        step_rag_num=4,
        seed=100,
        additional_default_tools=[]
    )
    agent.init_model()
    return agent

def process_final_report(agent, file, chatbot_state: List[Dict[str, str]]) -> Tuple[List[Dict[str, str]], Dict[str, Any]]:
    messages = chatbot_state if chatbot_state else []
    report_output = {"visible": False, "value": None}

    if file is None or not hasattr(file, "name"):
        messages.append({"role": "assistant", "content": "❌ Please upload a valid Excel file before analyzing."})
        return messages, report_output

    try:
        messages.append({"role": "user", "content": f"Processing Excel file: {os.path.basename(file.name)}"})
        messages.append({"role": "assistant", "content": "⏳ Extracting and analyzing data..."})

        extracted_text = extract_text_from_excel(file.name)
        chunks = split_text_into_chunks(extracted_text)
        chunk_responses = []

        for i, chunk in enumerate(chunks):
            messages.append({"role": "assistant", "content": f"🔍 Analyzing chunk {i+1}/{len(chunks)}..."})
            
            prompt = build_prompt_from_text(chunk)
            response = ""
            for result in agent.run_gradio_chat(
                message=prompt,
                history=[],
                temperature=0.2,
                max_new_tokens=MAX_NEW_TOKENS,
                max_token=MAX_TOKENS,
                call_agent=False,
                conversation=[],
            ):
                if isinstance(result, str):
                    response += result
                elif hasattr(result, "content"):
                    response += result.content
                elif isinstance(result, list):
                    for r in result:
                        if hasattr(r, "content"):
                            response += r.content
            
            chunk_responses.append(clean_response(response))
            messages.append({"role": "assistant", "content": f"✅ Chunk {i+1} analysis complete"})

        final_prompt = "\n\n".join(chunk_responses) + "\n\nSummarize the key findings above."
        messages.append({"role": "assistant", "content": "📊 Generating final report..."})

        stream_text = ""
        for result in agent.run_gradio_chat(
            message=final_prompt,
            history=[],
            temperature=0.2,
            max_new_tokens=MAX_NEW_TOKENS,
            max_token=MAX_TOKENS,
            call_agent=False,
            conversation=[],
        ):
            if isinstance(result, str):
                stream_text += result
            elif hasattr(result, "content"):
                stream_text += result.content
            elif isinstance(result, list):
                for r in result:
                    if hasattr(r, "content"):
                        stream_text += r.content
        
        final_report = f"# \U0001f9e0 Final Patient Report\n\n{clean_response(stream_text)}"
        messages[-1]["content"] = f"📊 Final Report:\n\n{clean_response(stream_text)}"

        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
        report_path = os.path.join(report_dir, f"report_{timestamp}.md")
        
        with open(report_path, 'w') as f:
            f.write(final_report)

        messages.append({"role": "assistant", "content": f"✅ Report generated and saved: report_{timestamp}.md"})
        report_output = {"visible": True, "value": report_path}

    except Exception as e:
        messages.append({"role": "assistant", "content": f"❌ Error processing file: {str(e)}"})

    return messages, report_output

def create_ui(agent):
    with gr.Blocks(title="Patient History Chat", css=".gradio-container {max-width: 900px !important}") as demo:
        gr.Markdown("## 🏥 Patient History Analysis Tool")
        
        with gr.Row():
            with gr.Column(scale=3):
                chatbot = gr.Chatbot(
                    label="Clinical Assistant",
                    show_copy_button=True,
                    height=600,
                    type="messages",
                    avatar_images=(
                        None,
                        "https://i.imgur.com/6wX7Zb4.png"
                    )
                )
            with gr.Column(scale=1):
                file_upload = gr.File(
                    label="Upload Excel File",
                    file_types=[".xlsx"],
                    height=100
                )
                analyze_btn = gr.Button(
                    "🧠 Analyze Patient History",
                    variant="primary"
                )
                report_output = gr.File(
                    label="Download Report",
                    visible=False,
                    interactive=False
                )

        # State to maintain chatbot messages
        chatbot_state = gr.State(value=[])

        def update_ui(file, current_state):
            messages, report_output = process_final_report(agent, file, current_state)
            return messages, report_output, messages

        analyze_btn.click(
            fn=update_ui,
            inputs=[file_upload, chatbot_state],
            outputs=[chatbot, report_output, chatbot_state],
            api_name="analyze"
        )

    return demo

if __name__ == "__main__":
    try:
        agent = init_agent()
        demo = create_ui(agent)
        demo.launch(
            server_name="0.0.0.0",
            server_port=7860,
            show_error=True,
            allowed_paths=["/data/hf_cache/reports"],
            share=False
        )
    except Exception as e:
        print(f"Error: {str(e)}")
        sys.exit(1)