ui/ui_core.py

import sys
import os
import pandas as pd
import pdfplumber
import gradio as gr
from typing import List

# ✅ 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:
    return text.encode("utf-8", "ignore").decode("utf-8")

def extract_all_text_from_csv_or_excel(file_path: str, progress=None, index=0, total=1) -> str:
    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"\U0001F4C4 {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:
    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"\U0001F4C4 {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 chunk_text(text: str, max_tokens: int = 8192) -> List[str]:
    chunks = []
    words = text.split()
    chunk = []
    token_count = 0
    for word in words:
        token_count += len(word) // 4 + 1
        if token_count > max_tokens:
            chunks.append(" ".join(chunk))
            chunk = [word]
            token_count = len(word) // 4 + 1
        else:
            chunk.append(word)
    if chunk:
        chunks.append(" ".join(chunk))
    return chunks

def create_ui(agent: TxAgent):
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("<h1 style='text-align: center;'>\U0001F4CB CPS: Clinical Patient Support System</h1>")

        chatbot = gr.Chatbot(label="CPS Assistant", height=600, type="messages", show_copy_button=True)
        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 centered loading message
                yield history + [{"role": "assistant", "content": "<div style='text-align:center'>⏳ Processing... Please wait while I analyze the files.</div>"}]

                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)

                all_responses = ""
                for i, chunk in enumerate(chunks):
                    full_message = (
                        f"{context}\n\n--- Uploaded File Chunk {i+1}/{len(chunks)} ---\n\n{chunk}\n\n--- End of Chunk ---\n\nNow begin your reasoning:"
                    )
                    generator = agent.run_gradio_chat(
                        message=full_message,
                        history=[],
                        temperature=0.3,
                        max_new_tokens=1024,
                        max_token=8192,
                        call_agent=False,
                        conversation=conversation,
                        uploaded_files=uploaded_files,
                        max_round=30
                    )
                    for update in generator:
                        if isinstance(update, str):
                            all_responses += update

                all_responses = sanitize_utf8(all_responses.strip())
                final_history = history + [
                    {"role": "user", "content": message},
                    {"role": "assistant", "content": all_responses}
                ]
                yield final_history

            except Exception as chat_error:
                print(f"Chat error: {chat_error}")
                final_history = history + [
                    {"role": "user", "content": message},
                    {"role": "assistant", "content": "❌ An error occurred while processing your request."}
                ]
                yield final_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