ui/ui_core.py

import sys
import os
import pandas as pd
import pdfplumber
import gradio as gr
import re
from typing import List, Dict, Optional

# ✅ Fix: Add src to Python path
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src")))

from txagent.txagent import TxAgent

def sanitize_utf8(text: str) -> str:
    """Clean text of problematic Unicode characters"""
    return text.encode('utf-8', 'ignore').decode('utf-8')

def clean_final_response(response: str) -> str:
    """Remove tool calls and other artifacts from final response"""
    # Split on TOOL_CALLS if present
    if '[TOOL_CALLS]' in response:
        response = response.split('[TOOL_CALLS]')[0]
    # Remove any remaining special tokens
    response = re.sub(r'\[[A-Z_]+\]', '', response)
    return response.strip()

def chunk_text(text: str, max_tokens: int = 8000) -> List[str]:
    """Split text into chunks based on token count estimate"""
    words = text.split()
    chunks = []
    current_chunk = []
    current_tokens = 0
    
    for word in words:
        # Estimate tokens (roughly 1 token per 4 characters)
        word_tokens = len(word) // 4 + 1
        if current_tokens + word_tokens > max_tokens and current_chunk:
            chunks.append(' '.join(current_chunk))
            current_chunk = [word]
            current_tokens = word_tokens
        else:
            current_chunk.append(word)
            current_tokens += word_tokens
    
    if current_chunk:
        chunks.append(' '.join(current_chunk))
    
    return chunks

def extract_all_text_from_csv_or_excel(file_path: str, progress=None, index=0, total=1) -> str:
    """Extract text from spreadsheet files with error handling"""
    try:
        if not os.path.exists(file_path):
            return f"File not found: {file_path}"

        if progress:
            progress((index + 1) / total, desc=f"Reading spreadsheet: {os.path.basename(file_path)}")

        if file_path.endswith(".csv"):
            df = pd.read_csv(file_path, encoding="utf-8", errors="replace", low_memory=False)
        elif file_path.endswith((".xls", ".xlsx")):
            df = pd.read_excel(file_path, engine="openpyxl")
        else:
            return f"Unsupported spreadsheet format: {file_path}"

        lines = []
        for _, row in df.iterrows():
            line = " | ".join(str(cell) for cell in row if pd.notna(cell))
            if line:
                lines.append(line)
        return f"📄 {os.path.basename(file_path)}\n\n" + "\n".join(lines)

    except Exception as e:
        return f"[Error reading {os.path.basename(file_path)}]: {str(e)}"

def extract_all_text_from_pdf(file_path: str, progress=None, index=0, total=1) -> str:
    """Extract text from PDF files with error handling"""
    try:
        if not os.path.exists(file_path):
            return f"PDF not found: {file_path}"

        extracted = []
        with pdfplumber.open(file_path) as pdf:
            num_pages = len(pdf.pages)
            for i, page in enumerate(pdf.pages):
                try:
                    text = page.extract_text() or ""
                    extracted.append(text.strip())
                    if progress:
                        progress((index + (i / num_pages)) / total, 
                               desc=f"Reading PDF: {os.path.basename(file_path)} ({i+1}/{num_pages})")
                except Exception as e:
                    extracted.append(f"[Error reading page {i+1}]: {str(e)}")
        return f"📄 {os.path.basename(file_path)}\n\n" + "\n\n".join(extracted)

    except Exception as e:
        return f"[Error reading PDF {os.path.basename(file_path)}]: {str(e)}"

def create_ui(agent: TxAgent):
    with gr.Blocks(theme=gr.themes.Soft(), title="Clinical Patient Support System") as demo:
        gr.Markdown("<h1 style='text-align: center;'>📋 CPS: Clinical Patient Support System</h1>")
        
        # Fix: Changed type to 'messages' to match Gradio requirements
        chatbot = gr.Chatbot(label="CPS Assistant", height=600, type="messages")
        
        file_upload = gr.File(
            label="Upload Medical File",
            file_types=[".pdf", ".txt", ".docx", ".jpg", ".png", ".csv", ".xls", ".xlsx"],
            file_count="multiple"
        )
        message_input = gr.Textbox(
            placeholder="Ask a biomedical question or just upload the files...", 
            show_label=False
        )
        send_button = gr.Button("Send", variant="primary")
        conversation_state = gr.State([])

        def handle_chat(message: str, history: list, conversation: list, uploaded_files: list, progress=gr.Progress()):
            context = (
                "You are an expert clinical AI assistant reviewing medical form or interview data. "
                "Your job is to analyze this data and reason about any information or red flags that a human doctor might have overlooked. "
                "Provide a **detailed and structured response**, including examples, supporting evidence from the form, and clinical rationale for why these items matter. "
                "Ensure the output is informative and helpful for improving patient care. "
                "Do not hallucinate. Base the response only on the provided form content. "
                "End with a section labeled '🧠 Final Analysis' where you summarize key findings the doctor may have missed."
            )

            try:
                # Show processing message immediately
                history.append((message, "⏳ Processing your request..."))
                yield history

                extracted_text = ""
                if uploaded_files and isinstance(uploaded_files, list):
                    total_files = len(uploaded_files)
                    for index, file in enumerate(uploaded_files):
                        if not hasattr(file, 'name'):
                            continue
                        path = file.name
                        try:
                            if path.endswith((".csv", ".xls", ".xlsx")):
                                extracted_text += extract_all_text_from_csv_or_excel(path, progress, index, total_files) + "\n"
                            elif path.endswith(".pdf"):
                                extracted_text += extract_all_text_from_pdf(path, progress, index, total_files) + "\n"
                            else:
                                extracted_text += f"(Uploaded file: {os.path.basename(path)})\n"
                        except Exception as file_error:
                            extracted_text += f"[Error processing {os.path.basename(path)}]: {str(file_error)}\n"

                sanitized = sanitize_utf8(extracted_text.strip())
                chunks = chunk_text(sanitized)

                full_response = ""
                for i, chunk in enumerate(chunks):
                    chunked_prompt = (
                        f"{context}\n\n--- Uploaded File Content (Chunk {i+1}/{len(chunks)}) ---\n\n{chunk}\n\n"
                        f"--- End of Chunk ---\n\nNow begin your analysis:"
                    )

                    generator = agent.run_gradio_chat(
                        message=chunked_prompt,
                        history=[],
                        temperature=0.3,
                        max_new_tokens=1024,
                        max_token=8192,
                        call_agent=False,
                        conversation=conversation,
                        uploaded_files=uploaded_files,
                        max_round=30
                    )

                    # Collect all updates from the generator
                    chunk_response = ""
                    for update in generator:
                        if isinstance(update, str):
                            chunk_response += update
                        elif isinstance(update, list):
                            # Handle list of messages
                            for msg in update:
                                if hasattr(msg, 'content'):
                                    chunk_response += msg.content

                    full_response += chunk_response + "\n\n"

                # Clean up the final response
                full_response = clean_final_response(full_response.strip())
                
                # Remove the processing message and add the final response
                history[-1] = (message, full_response)
                yield history

            except Exception as chat_error:
                print(f"Chat handling error: {chat_error}")
                error_msg = "An error occurred while processing your request. Please try again."
                if len(history) > 0 and history[-1][1].startswith("⏳"):
                    history[-1] = (history[-1][0], error_msg)
                else:
                    history.append((message, error_msg))
                yield history

        inputs = [message_input, chatbot, conversation_state, file_upload]
        send_button.click(fn=handle_chat, inputs=inputs, outputs=chatbot)
        message_input.submit(fn=handle_chat, inputs=inputs, outputs=chatbot)

        gr.Examples([
            ["Upload your medical form and ask what the doctor might've missed."],
            ["This patient was treated with antibiotics for UTI. What else should we check?"],
            ["Is there anything abnormal in the attached blood work report?"]
        ], inputs=message_input)

    return demo