ui/ui_core.py

import sys
import os
import pandas as pd
import pdfplumber
import gradio as gr
import re
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 re.sub(r'[\ud800-\udfff]', '', text)

def chunk_text(text: str, max_tokens=8000) -> List[str]:
    chunks = []
    lines = text.split("\n")
    current_chunk = []
    current_tokens = 0
    for line in lines:
        line_tokens = len(line.split())
        if current_tokens + line_tokens > max_tokens:
            chunks.append("\n".join(current_chunk))
            current_chunk = [line]
            current_tokens = line_tokens
        else:
            current_chunk.append(line)
            current_tokens += line_tokens
    if current_chunk:
        chunks.append("\n".join(current_chunk))
    return chunks

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"📄 {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"📄 {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()) as demo:
        gr.Markdown("<h1 style='text-align: center;'>📋 CPS: Clinical Patient Support System</h1>")
        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:
                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 file: {os.path.basename(path)}] — {str(file_error)}\n"
                            continue

                sanitized = sanitize_utf8(extracted_text.strip())
                chunks = chunk_text(sanitized, max_tokens=8000)

                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--- End of Chunk ---\n\nNow begin your reasoning:"
                    )

                    generator = agent.run_gradio_chat(
                        message=chunked_prompt,
                        history=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:
                        try:
                            if isinstance(update, list):
                                cleaned = [msg for msg in update if hasattr(msg, 'role') and hasattr(msg, 'content')]
                                if cleaned:
                                    yield cleaned
                            elif isinstance(update, str):
                                yield sanitize_utf8(update.encode("utf-8", "replace").decode("utf-8"))
                        except Exception as update_error:
                            print(f"Error processing update: {update_error}")
                            continue

            except Exception as chat_error:
                print(f"Chat handling error: {chat_error}")
                yield "An error occurred while processing your request. Please try again."

        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