ui/ui_core.py

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

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

from txagent.txagent import TxAgent

def safe_extract_table_data(table: List[List[str]]) -> List[str]:
    extracted_rows = []
    if not table or not isinstance(table, list):
        return extracted_rows
    for row in table:
        if not row or not isinstance(row, list):
            continue
        try:
            clean_row = [str(cell) if cell is not None else "" for cell in row]
            if any(clean_row):
                extracted_rows.append("\t".join(clean_row))
        except Exception as e:
            print(f"Error processing table row: {e}")
            continue
    return extracted_rows

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 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}"

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

        group_column = None
        for col in ["Booking Number", "Form Name"]:
            if col in df.columns:
                group_column = col
                break

        if group_column:
            try:
                groups = df.groupby(group_column)
                result = []
                for group_name, group_df in groups:
                    if group_name is None:
                        continue
                    result.append(f"\n### Group: {group_name}\n")
                    result.append(tabulate(group_df, headers="keys", tablefmt="github", showindex=False))
                return "\n".join(result) if result else tabulate(df, headers="keys", tablefmt="github", showindex=False)
            except Exception as e:
                print(f"Error during grouping: {e}")
                return tabulate(df, headers="keys", tablefmt="github", showindex=False)
        else:
            return tabulate(df, headers="keys", tablefmt="github", showindex=False)

    except Exception as e:
        return f"Error parsing file {os.path.basename(file_path)}: {str(e)}"

def extract_all_text_from_pdf(file_path: str, progress=None, index=0, total=1) -> str:
    extracted = []
    try:
        if not os.path.exists(file_path):
            return f"PDF file not found: {file_path}"

        with pdfplumber.open(file_path) as pdf:
            num_pages = len(pdf.pages) if hasattr(pdf, 'pages') else 0
            for i, page in enumerate(pdf.pages if num_pages > 0 else []):
                try:
                    tables = page.extract_tables() if hasattr(page, 'extract_tables') else []
                    for table in tables if tables else []:
                        extracted.extend(safe_extract_table_data(table))
                    
                    if progress and num_pages > 0:
                        progress((index + (i / num_pages)) / total, 
                                desc=f"Parsing PDF: {os.path.basename(file_path)} ({i+1}/{num_pages})")
                except Exception as page_error:
                    print(f"Error processing page {i+1}: {page_error}")
                    continue

        return "\n".join(extracted) if extracted else f"No extractable content found in {os.path.basename(file_path)}"
    except Exception as e:
        return f"Error parsing 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"
                                if progress:
                                    progress((index + 1) / total_files, desc=f"Skipping unsupported file: {os.path.basename(path)}")
                        except Exception as file_error:
                            print(f"Error processing file {path}: {file_error}")
                            extracted_text += f"\n[Error processing file: {os.path.basename(path)}]\n"
                            continue

                    message = f"{context}\n\n---\n{extracted_text.strip()}\n---\n\nBegin your reasoning."

                final_response = None
                generator = agent.run_gradio_chat(
                    message=message,
                    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 not (
                                    msg.role == "assistant"
                                    and hasattr(msg, 'content')
                                    and msg.content.strip().startswith("🧰")
                                )
                            ]
                            if cleaned:
                                final_response = cleaned
                                yield cleaned
                        else:
                            if isinstance(update, str) and not update.strip().startswith("🧰"):
                                yield 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