app.py

import os
import io
import json
import csv
import asyncio
import xml.etree.ElementTree as ET
from typing import Any, Dict, Optional, Tuple, Union, List

import httpx
import gradio as gr
import torch
from dotenv import load_dotenv
from loguru import logger
from huggingface_hub import login
from openai import OpenAI
from reportlab.pdfgen import canvas
from transformers import (
    AutoTokenizer,
    AutoModelForSequenceClassification,
    MarianMTModel,
    MarianTokenizer,
)
import pandas as pd
import altair as alt
import spacy
import spacy.cli
import PyPDF2

# Ensure spaCy model is downloaded
try:
    nlp = spacy.load("en_core_web_sm")
except OSError:
    logger.info("Downloading SpaCy 'en_core_web_sm' model...")
    spacy.cli.download("en_core_web_sm")
    nlp = spacy.load("en_core_web_sm")

# Logging
logger.add("error_logs.log", rotation="1 MB", level="ERROR")

# Load environment variables
load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HF_TOKEN")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
ENTREZ_EMAIL = os.getenv("ENTREZ_EMAIL")

# Basic checks
if not HUGGINGFACE_TOKEN or not OPENAI_API_KEY:
    logger.error("Missing Hugging Face or OpenAI credentials.")
    raise ValueError("Missing credentials for Hugging Face or OpenAI.")

# API endpoints
PUBMED_SEARCH_URL = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
PUBMED_FETCH_URL = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi"
EUROPE_PMC_BASE_URL = "https://www.ebi.ac.uk/europepmc/webservices/rest/search"

# Log in to Hugging Face
login(HUGGINGFACE_TOKEN)

# Initialize OpenAI
client = OpenAI(api_key=OPENAI_API_KEY)

# Device setting
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger.info(f"Using device: {device}")

# Model settings
MODEL_NAME = "mgbam/bert-base-finetuned-mgbam"
try:
    model = AutoModelForSequenceClassification.from_pretrained(
        MODEL_NAME, use_auth_token=HUGGINGFACE_TOKEN
    ).to(device)
    tokenizer = AutoTokenizer.from_pretrained(
        MODEL_NAME, use_auth_token=HUGGINGFACE_TOKEN
    )
except Exception as e:
    logger.error(f"Model load error: {e}")
    raise

# Translation model settings
try:
    translation_model_name = "Helsinki-NLP/opus-mt-en-fr"
    translation_model = MarianMTModel.from_pretrained(
        translation_model_name, use_auth_token=HUGGINGFACE_TOKEN
    ).to(device)
    translation_tokenizer = MarianTokenizer.from_pretrained(
        translation_model_name, use_auth_token=HUGGINGFACE_TOKEN
    )
except Exception as e:
    logger.error(f"Translation model load error: {e}")
    raise

LANGUAGE_MAP: Dict[str, Tuple[str, str]] = {
    "English to French": ("en", "fr"),
    "French to English": ("fr", "en"),
}

###################################################
#                     UTILS                       #
###################################################

def safe_json_parse(text: str) -> Union[Dict, None]:
    """Safely parse JSON string into a Python dictionary."""
    try:
        return json.loads(text)
    except json.JSONDecodeError as e:
        logger.error(f"JSON parsing error: {e}")
        return None

def parse_pubmed_xml(xml_data: str) -> List[Dict[str, Any]]:
    """Parses PubMed XML data and returns a list of structured articles."""
    root = ET.fromstring(xml_data)
    articles = []
    for article in root.findall(".//PubmedArticle"):
        pmid = article.findtext(".//PMID")
        title = article.findtext(".//ArticleTitle")
        abstract = article.findtext(".//AbstractText")
        journal = article.findtext(".//Journal/Title")
        pub_date_elem = article.find(".//JournalIssue/PubDate")
        pub_date = None
        if pub_date_elem is not None:
            year = pub_date_elem.findtext("Year")
            month = pub_date_elem.findtext("Month")
            day = pub_date_elem.findtext("Day")
            if year and month and day:
                pub_date = f"{year}-{month}-{day}"
            else:
                pub_date = year
        articles.append({
            "PMID": pmid,
            "Title": title,
            "Abstract": abstract,
            "Journal": journal,
            "PublicationDate": pub_date,
        })
    return articles

###################################################
#                ASYNC FETCHES                    #
###################################################

async def fetch_articles_by_nct_id(nct_id: str) -> Dict[str, Any]:
    params = {"query": nct_id, "format": "json"}
    async with httpx.AsyncClient() as client_http:
        try:
            response = await client_http.get(EUROPE_PMC_BASE_URL, params=params)
            response.raise_for_status()
            return response.json()
        except Exception as e:
            logger.error(f"Error fetching articles for {nct_id}: {e}")
            return {"error": str(e)}

async def fetch_articles_by_query(query_params: str) -> Dict[str, Any]:
    parsed_params = safe_json_parse(query_params)
    if not parsed_params or not isinstance(parsed_params, dict):
        return {"error": "Invalid JSON."}
    query_string = " AND ".join(f"{k}:{v}" for k, v in parsed_params.items())
    params = {"query": query_string, "format": "json"}
    async with httpx.AsyncClient() as client_http:
        try:
            response = await client_http.get(EUROPE_PMC_BASE_URL, params=params)
            response.raise_for_status()
            return response.json()
        except Exception as e:
            logger.error(f"Error fetching articles: {e}")
            return {"error": str(e)}

async def fetch_pubmed_by_query(query_params: str) -> Dict[str, Any]:
    parsed_params = safe_json_parse(query_params)
    if not parsed_params or not isinstance(parsed_params, dict):
        return {"error": "Invalid JSON for PubMed."}

    search_params = {
        "db": "pubmed",
        "retmode": "json",
        "email": ENTREZ_EMAIL,
        "retmax": parsed_params.get("retmax", "10"),
        "term": parsed_params.get("term", ""),
    }

    async with httpx.AsyncClient() as client_http:
        try:
            search_response = await client_http.get(PUBMED_SEARCH_URL, params=search_params)
            search_response.raise_for_status()
            search_data = search_response.json()
            id_list = search_data.get("esearchresult", {}).get("idlist", [])
            if not id_list:
                return {"result": ""}

            fetch_params = {
                "db": "pubmed",
                "id": ",".join(id_list),
                "retmode": "xml",
                "email": ENTREZ_EMAIL,
            }
            fetch_response = await client_http.get(PUBMED_FETCH_URL, params=fetch_params)
            fetch_response.raise_for_status()
            return {"result": fetch_response.text}
        except Exception as e:
            logger.error(f"Error fetching PubMed articles: {e}")
            return {"error": str(e)}

async def fetch_crossref_by_query(query_params: str) -> Dict[str, Any]:
    parsed_params = safe_json_parse(query_params)
    if not parsed_params or not isinstance(parsed_params, dict):
        return {"error": "Invalid JSON for Crossref."}
    CROSSREF_API_URL = "https://api.crossref.org/works"
    async with httpx.AsyncClient() as client_http:
        try:
            response = await client_http.get(CROSSREF_API_URL, params=parsed_params)
            response.raise_for_status()
            return response.json()
        except Exception as e:
            logger.error(f"Error fetching Crossref data: {e}")
            return {"error": str(e)}

###################################################
#                   CORE LOGIC                    #
###################################################

def summarize_text(text: str) -> str:
    """Summarize text using OpenAI."""
    if not text.strip():
        return "No text provided for summarization."
    try:
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": f"Summarize the following clinical data:\n{text}"}],
            max_tokens=200,
            temperature=0.7,
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        logger.error(f"Summarization Error: {e}")
        return "Summarization failed."

def predict_outcome(text: str) -> Union[Dict[str, float], str]:
    """Predict outcomes (classification) using a fine-tuned model."""
    if not text.strip():
        return "No text provided for prediction."
    try:
        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
        inputs = {k: v.to(device) for k, v in inputs.items()}
        with torch.no_grad():
            outputs = model(**inputs)
        probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)[0]
        return {f"Label {i+1}": float(prob.item()) for i, prob in enumerate(probabilities)}
    except Exception as e:
        logger.error(f"Prediction Error: {e}")
        return "Prediction failed."

def generate_report(text: str, filename: str = "clinical_report.pdf") -> Optional[str]:
    """Generate a PDF report from the given text."""
    try:
        if not text.strip():
            logger.warning("No text provided for the report.")
        c = canvas.Canvas(filename)
        c.drawString(100, 750, "Clinical Research Report")
        lines = text.split("\n")
        y = 730
        for line in lines:
            if y < 50:
                c.showPage()
                y = 750
            c.drawString(100, y, line)
            y -= 15
        c.save()
        logger.info(f"Report generated: {filename}")
        return filename
    except Exception as e:
        logger.error(f"Report Generation Error: {e}")
        return None

def visualize_predictions(predictions: Dict[str, float]) -> Optional[alt.Chart]:
    """Visualize model prediction probabilities using Altair."""
    try:
        data = pd.DataFrame(list(predictions.items()), columns=["Label", "Probability"])
        chart = (
            alt.Chart(data)
            .mark_bar()
            .encode(
                x=alt.X("Label:N", sort=None),
                y="Probability:Q",
                tooltip=["Label", "Probability"],
            )
            .properties(title="Prediction Probabilities", width=500, height=300)
        )
        return chart
    except Exception as e:
        logger.error(f"Visualization Error: {e}")
        return None

def translate_text(text: str, translation_option: str) -> str:
    """Translate text between English and French."""
    if not text.strip():
        return "No text provided for translation."
    try:
        if translation_option not in LANGUAGE_MAP:
            return "Unsupported translation option."
        inputs = translation_tokenizer(text, return_tensors="pt", padding=True).to(device)
        translated_tokens = translation_model.generate(**inputs)
        return translation_tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
    except Exception as e:
        logger.error(f"Translation Error: {e}")
        return "Translation failed."

def perform_named_entity_recognition(text: str) -> str:
    """Perform Named Entity Recognition (NER) using spaCy."""
    if not text.strip():
        return "No text provided for NER."
    try:
        doc = nlp(text)
        entities = [(ent.text, ent.label_) for ent in doc.ents]
        if not entities:
            return "No named entities found."
        return "\n".join(f"{ent_text} -> {ent_label}" for ent_text, ent_label in entities)
    except Exception as e:
        logger.error(f"NER Error: {e}")
        return "Named Entity Recognition failed."

###################################################
#                ENHANCED EDA                     #
###################################################

def perform_enhanced_eda(df: pd.DataFrame) -> Tuple[str, Optional[alt.Chart], Optional[alt.Chart]]:
    """
    Show columns, shape, numeric summary, correlation heatmap, and distribution histograms.
    Returns (text_summary, correlation_chart, distribution_chart).
    """
    try:
        columns_info = f"Columns: {list(df.columns)}"
        shape_info = f"Shape: {df.shape[0]} rows x {df.shape[1]} columns"

        with pd.option_context("display.max_colwidth", 200, "display.max_rows", None):
            describe_info = df.describe(include="all").to_string()

        summary_text = (
            f"--- Enhanced EDA Summary ---\n"
            f"{columns_info}\n{shape_info}\n\n"
            f"Summary Statistics:\n{describe_info}\n"
        )

        numeric_cols = df.select_dtypes(include="number")
        corr_chart = None
        if numeric_cols.shape[1] >= 2:
            corr = numeric_cols.corr()
            corr_melted = corr.reset_index().melt(id_vars="index")
            corr_melted.columns = ["Feature1", "Feature2", "Correlation"]
            corr_chart = (
                alt.Chart(corr_melted)
                .mark_rect()
                .encode(
                    x="Feature1:O",
                    y="Feature2:O",
                    color="Correlation:Q",
                    tooltip=["Feature1", "Feature2", "Correlation"]
                )
                .properties(width=400, height=400, title="Correlation Heatmap")
            )

        distribution_chart = None
        if numeric_cols.shape[1] >= 1:
            df_long = numeric_cols.melt(var_name='Column', value_name='Value')
            distribution_chart = (
                alt.Chart(df_long)
                .mark_bar()
                .encode(
                    alt.X("Value:Q", bin=alt.Bin(maxbins=30)),
                    alt.Y('count()'),
                    alt.Facet('Column:N', columns=2),
                    tooltip=["Value"]
                )
                .properties(
                    title='Distribution of Numeric Columns',
                    width=300,
                    height=200
                )
                .interactive()
            )

        return summary_text, corr_chart, distribution_chart

    except Exception as e:
        logger.error(f"Enhanced EDA Error: {e}")
        return f"Enhanced EDA failed: {e}", None, None

###################################################
#                FILE PARSING                     #
###################################################

def parse_text_file(uploaded_file: gr.File) -> str:
    """Reads a .txt file as UTF-8 text."""
    return uploaded_file.read().decode("utf-8")

def parse_csv_file(uploaded_file: gr.File) -> pd.DataFrame:
    """
    Reads CSV content with possible BOM issues 
    by trying 'utf-8' and 'utf-8-sig'.
    """
    content = uploaded_file.read().decode("utf-8", errors="replace")
    # We can attempt to parse with multiple encodings if needed:
    # For simplicity, let's just do a fallback approach:
    try:
        from io import StringIO
        df = pd.read_csv(StringIO(content))
        return df
    except Exception as e:
        raise ValueError(f"CSV parse error: {e}")

def parse_excel_file(uploaded_file: gr.File) -> pd.DataFrame:
    """
    Parse an Excel file into a pandas DataFrame.
    1) If the path exists, read directly from path.
    2) Else read from uploaded_file.file (in-memory) in binary mode.
    """
    import pandas as pd
    import os

    excel_path = uploaded_file.name
    # Try local path first
    if os.path.isfile(excel_path):
        return pd.read_excel(excel_path, engine="openpyxl")

    # Fall back to reading raw bytes from uploaded_file.file
    try:
        excel_bytes = uploaded_file.file.read()
        return pd.read_excel(io.BytesIO(excel_bytes), engine="openpyxl")
    except Exception as e:
        raise ValueError(f"Excel parse error: {e}")

def parse_pdf_file(uploaded_file: gr.File) -> str:
    """Reads a PDF file with PyPDF2, extracting text from each page."""
    try:
        pdf_reader = PyPDF2.PdfReader(uploaded_file)
        text_content = []
        for page in pdf_reader.pages:
            text_content.append(page.extract_text())
        return "\n".join(text_content)
    except Exception as e:
        logger.error(f"PDF parse error: {e}")
        return f"Error reading PDF file: {e}"

###################################################
#             GRADIO INTERFACE                    #
###################################################

with gr.Blocks() as demo:
    gr.Markdown("# ✨ Advanced Clinical Research Assistant with Enhanced EDA ✨")
    gr.Markdown("""
Welcome to the **Enhanced** AI-Powered Clinical Assistant!  
- **Summarize** large blocks of clinical text.  
- **Predict** outcomes with a fine-tuned model.  
- **Translate** text (English ↔ French).  
- **Perform Named Entity Recognition** (spaCy).  
- **Fetch** from PubMed, Crossref, Europe PMC.  
- **Generate** professional PDF reports.  
- **Perform Enhanced EDA** on CSV/Excel data (correlation heatmaps + distribution plots).  
""")
    
    # Inputs
    with gr.Row():
        text_input = gr.Textbox(label="Input Text", lines=5, placeholder="Enter clinical text or query...")
        # We'll rely on .name and .file for the path and file handle
        file_input = gr.File(
            label="Upload File (txt/csv/xls/xlsx/pdf)",
            file_types=[".txt", ".csv", ".xls", ".xlsx", ".pdf"]
        )
    
    action = gr.Radio(
        [
            "Summarize",
            "Predict Outcome",
            "Generate Report",
            "Translate",
            "Perform Named Entity Recognition",
            "Perform Enhanced EDA",
            "Fetch Clinical Studies",
            "Fetch PubMed Articles (Legacy)",
            "Fetch PubMed by Query",
            "Fetch Crossref by Query",
        ],
        label="Select an Action",
    )
    translation_option = gr.Dropdown(
        choices=list(LANGUAGE_MAP.keys()), 
        label="Translation Option", 
        value="English to French"
    )
    query_params_input = gr.Textbox(
        label="Query Parameters (JSON Format)",
        placeholder='{"term": "cancer", "retmax": "5"}'
    )
    nct_id_input = gr.Textbox(label="NCT ID for Article Search")
    report_filename_input = gr.Textbox(
        label="Report Filename",
        placeholder="clinical_report.pdf",
        value="clinical_report.pdf"
    )
    export_format = gr.Dropdown(["None", "CSV", "JSON"], label="Export Format")
    
    # Outputs
    output_text = gr.Textbox(label="Output", lines=10)
    with gr.Row():
        output_chart = gr.Plot(label="Visualization 1")
        output_chart2 = gr.Plot(label="Visualization 2")
    output_file = gr.File(label="Generated File")
    
    submit_button = gr.Button("Submit")
    
    ################################################################
    #                    MAIN HANDLER FUNCTION                     #
    ################################################################
    
    async def handle_action(
        action: str,
        text: str,
        file_up: gr.File,
        translation_opt: str,
        query_params: str,
        nct_id: str,
        report_filename: str,
        export_format: str
    ) -> Tuple[Optional[str], Optional[Any], Optional[Any], Optional[str]]:
        
        # 1) Start with user-provided text
        combined_text = text.strip()
        
        # 2) If user uploaded a file, parse it based on extension
        if file_up is not None:
            file_ext = os.path.splitext(file_up.name)[1].lower()
            
            if file_ext == ".txt":
                file_text = parse_text_file(file_up)
                combined_text = (combined_text + "\n" + file_text).strip()
            
            elif file_ext == ".csv":
                # If user chose EDA, we'll parse into DataFrame below
                # If we just want to combine text for Summarize, etc., do so:
                pass  
            
            elif file_ext in [".xls", ".xlsx"]:
                # We'll handle Excel parsing in the EDA step if needed
                pass
            
            elif file_ext == ".pdf":
                file_text = parse_pdf_file(file_up)
                combined_text = (combined_text + "\n" + file_text).strip()
        
        ### ACTIONS ###
        if action == "Summarize":
            if file_up and file_up.name.endswith(".csv"):
                # Merge CSV text into combined_text 
                # in case user wants summarization of the CSV's raw text
                try:
                    df_csv = parse_csv_file(file_up)
                    # Turn CSV into text
                    csv_as_text = df_csv.to_csv(index=False)
                    combined_text = (combined_text + "\n" + csv_as_text).strip()
                except Exception as e:
                    return f"CSV parse error for Summarize: {e}", None, None, None
            
            # Summarize the combined text
            return summarize_text(combined_text), None, None, None
        
        elif action == "Predict Outcome":
            return _action_predict_outcome(combined_text, file_up)
        
        elif action == "Generate Report":
            # Add CSV content if needed
            if file_up and file_up.name.endswith(".csv"):
                try:
                    df_csv = parse_csv_file(file_up)
                    combined_text += "\n" + df_csv.to_csv(index=False)
                except Exception as e:
                    logger.error(f"Error reading CSV for report: {e}")
            file_path = generate_report(combined_text, filename=report_filename)
            msg = f"Report generated: {file_path}" if file_path else "Report generation failed."
            return msg, None, None, file_path
        
        elif action == "Translate":
            # Optionally read CSV or PDF text?
            if file_up and file_up.name.endswith(".csv"):
                try:
                    df_csv = parse_csv_file(file_up)
                    combined_text += "\n" + df_csv.to_csv(index=False)
                except Exception as e:
                    return f"CSV parse error for Translate: {e}", None, None, None
            translated = translate_text(combined_text, translation_opt)
            return translated, None, None, None
        
        elif action == "Perform Named Entity Recognition":
            # Merge CSV as text if user wants NER on CSV
            if file_up and file_up.name.endswith(".csv"):
                try:
                    df_csv = parse_csv_file(file_up)
                    combined_text += "\n" + df_csv.to_csv(index=False)
                except Exception as e:
                    return f"CSV parse error for NER: {e}", None, None, None
            ner_result = perform_named_entity_recognition(combined_text)
            return ner_result, None, None, None
        
        elif action == "Perform Enhanced EDA":
            return await _action_eda(combined_text, file_up, text)
        
        elif action == "Fetch Clinical Studies":
            if nct_id:
                result = await fetch_articles_by_nct_id(nct_id)
            elif query_params:
                result = await fetch_articles_by_query(query_params)
            else:
                return "Provide either an NCT ID or valid query parameters.", None, None, None
            
            articles = result.get("resultList", {}).get("result", [])
            if not articles:
                return "No articles found.", None, None, None
            
            formatted_results = "\n\n".join(
                f"Title: {a.get('title')}\nJournal: {a.get('journalTitle')} ({a.get('pubYear')})"
                for a in articles
            )
            return formatted_results, None, None, None
        
        elif action in ["Fetch PubMed Articles (Legacy)", "Fetch PubMed by Query"]:
            pubmed_result = await fetch_pubmed_by_query(query_params)
            xml_data = pubmed_result.get("result")
            if xml_data:
                articles = parse_pubmed_xml(xml_data)
                if not articles:
                    return "No articles found.", None, None, None
                formatted = "\n\n".join(
                    f"{a['Title']} - {a['Journal']} ({a['PublicationDate']})"
                    for a in articles if a['Title']
                )
                return formatted if formatted else "No articles found.", None, None, None
            return "No articles found or error fetching data.", None, None, None
        
        elif action == "Fetch Crossref by Query":
            crossref_result = await fetch_crossref_by_query(query_params)
            items = crossref_result.get("message", {}).get("items", [])
            if not items:
                return "No results found.", None, None, None
            formatted = "\n\n".join(
                f"Title: {item.get('title', ['No title'])[0]}, DOI: {item.get('DOI')}"
                for item in items
            )
            return formatted, None, None, None
        
        return "Invalid action.", None, None, None

    def _action_predict_outcome(combined_text: str, file_up: gr.File) -> Tuple[Optional[str], Optional[Any], Optional[Any], Optional[str]]:
        # If CSV is uploaded, we can merge it into text or do separate logic
        if file_up and file_up.name.endswith(".csv"):
            try:
                df_csv = parse_csv_file(file_up)
                # Optionally, merge CSV content into the text to be classified
                combined_text_local = combined_text + "\n" + df_csv.to_csv(index=False)
            except Exception as e:
                return f"CSV parse error for Predict Outcome: {e}", None, None, None
        else:
            combined_text_local = combined_text
        
        predictions = predict_outcome(combined_text_local)
        if isinstance(predictions, dict):
            chart = visualize_predictions(predictions)
            return json.dumps(predictions, indent=2), chart, None, None
        return predictions, None, None, None
    
    async def _action_eda(combined_text: str, file_up: Optional[gr.File], raw_text: str) -> Tuple[Optional[str], Optional[Any], Optional[Any], Optional[str]]:
        """
        Perform Enhanced EDA on a CSV or Excel file if uploaded.
        If .csv is present, parse as CSV; if .xls/.xlsx is present, parse as Excel.
        """
        # Make sure we either have a file or some data in the text
        if not file_up and not raw_text.strip():
            return "No data provided for EDA.", None, None, None
        
        if file_up:
            file_ext = os.path.splitext(file_up.name)[1].lower()
            if file_ext == ".csv":
                try:
                    df_csv = parse_csv_file(file_up)
                    eda_summary, corr_chart, dist_chart = perform_enhanced_eda(df_csv)
                    return eda_summary, corr_chart, dist_chart, None
                except Exception as e:
                    return f"CSV EDA failed: {e}", None, None, None
            
            elif file_ext in [".xls", ".xlsx"]:
                try:
                    df_excel = parse_excel_file(file_up)
                    eda_summary, corr_chart, dist_chart = perform_enhanced_eda(df_excel)
                    return eda_summary, corr_chart, dist_chart, None
                except Exception as e:
                    return f"Excel EDA failed: {e}", None, None, None
            
            else:
                # EDA not supported for PDF or .txt in this example
                return "No valid CSV/Excel data found for EDA.", None, None, None
        else:
            # If no file, maybe the user pasted CSV into the text box
            if "," in raw_text:
                # Attempt to parse text as CSV
                try:
                    from io import StringIO
                    df_csv = pd.read_csv(StringIO(raw_text))
                    eda_summary, corr_chart, dist_chart = perform_enhanced_eda(df_csv)
                    return eda_summary, corr_chart, dist_chart, None
                except Exception as e:
                    return f"EDA parse error for pasted CSV: {e}", None, None, None
            return "No valid CSV/Excel data found for EDA.", None, None, None

    submit_button.click(
        handle_action,
        inputs=[
            action,
            text_input,
            file_input,
            translation_option,
            query_params_input,
            nct_id_input,
            report_filename_input,
            export_format,
        ],
        outputs=[
            output_text,
            output_chart,
            output_chart2,
            output_file,
        ],
    )

demo.launch(server_name="0.0.0.0", server_port=7860, share=True)