app.py

import streamlit as st
import pandas as pd
import torch
import re 
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from peft import PeftModel
from text_processing import TextProcessor
import gc
from pathlib import Path

# Configure page
st.set_page_config(
    page_title="Biomedical Papers Analysis",
    page_icon="🔬",
    layout="wide"
)

# Initialize session state
if 'processed_data' not in st.session_state:
    st.session_state.processed_data = None
if 'summaries' not in st.session_state:
    st.session_state.summaries = None
if 'text_processor' not in st.session_state:
    st.session_state.text_processor = None
if 'processing_started' not in st.session_state:
    st.session_state.processing_started = False
if 'focused_summary_generated' not in st.session_state:
    st.session_state.focused_summary_generated = False

def load_model(model_type):
    """Load appropriate model based on type with proper memory management"""
    try:
        # Clear any existing cached data
        gc.collect()
        torch.cuda.empty_cache()
        
        device = "cpu"  # Force CPU usage
        
        if model_type == "summarize":
            # Load the new fine-tuned model directly
            model = AutoModelForSeq2SeqLM.from_pretrained(
                "pendar02/bart-large-pubmedd",
                cache_dir="./models",
                torch_dtype=torch.float32
            ).to(device)
            
            tokenizer = AutoTokenizer.from_pretrained(
                "pendar02/bart-large-pubmedd",
                cache_dir="./models"
            )
        else:  # question_focused
            base_model = AutoModelForSeq2SeqLM.from_pretrained(
                "GanjinZero/biobart-base",
                cache_dir="./models",
                torch_dtype=torch.float32
            ).to(device)
            
            model = PeftModel.from_pretrained(
                base_model, 
                "pendar02/biobart-finetune",
                is_trainable=False
            ).to(device)
            
            tokenizer = AutoTokenizer.from_pretrained(
                "GanjinZero/biobart-base",
                cache_dir="./models"
            )
        
        model.eval()
        return model, tokenizer
    except Exception as e:
        st.error(f"Error loading model: {str(e)}")
        raise

def cleanup_model(model, tokenizer):
    """Properly cleanup model resources"""
    try:
        del model
        del tokenizer
        torch.cuda.empty_cache()
        gc.collect()
    except Exception:
        pass

def validate_excel_structure(df):
    """Validate the structure and content of the Excel file"""
    validation_messages = []
    
    # Check for minimum content
    if len(df) == 0:
        validation_messages.append("File contains no data")
        return False, validation_messages
    
    # Check abstract length
    if df['Abstract'].str.len().min() < 50:
        validation_messages.append("Some abstracts are too short (less than 50 characters)")
    
    # Check publication year format
    try:
        df['Publication Year'] = df['Publication Year'].astype(int)
        if df['Publication Year'].min() < 1900 or df['Publication Year'].max() > 2025:
            validation_messages.append("Invalid publication years detected")
    except:
        validation_messages.append("Invalid format in Publication Year column")
    
    # Check if DOIs are in valid format (basic check)
    if not df['DOI'].str.contains(r'10\.\d{4,}/.+', na=True).all():
        validation_messages.append("Some DOIs are in invalid format")
    
    return len(validation_messages) == 0, validation_messages


@st.cache_data
def process_excel(uploaded_file):
    """Process uploaded Excel file"""
    try:
        df = pd.read_excel(uploaded_file)
        required_columns = ['Abstract', 'Article Title', 'Authors', 
                          'Source Title', 'Publication Year', 'DOI', 'Times Cited, All Databases']
        
        # Check required columns
        missing_columns = [col for col in required_columns if col not in df.columns]
        if missing_columns:
            st.error(f"Missing required columns: {', '.join(missing_columns)}")
            return None

        # Check number of papers
        if len(df) > 5:
            st.error("❌ Your file contains more than 5 papers. Please upload a file with maximum 5 papers.")
            return None

        # Validate structure and content
        is_valid, messages = validate_excel_structure(df)
        if not is_valid:
            for msg in messages:
                st.error(f"❌ {msg}")
            return None


        # Check for empty required fields
        for col in required_columns:
            if df[col].isna().any():
                st.warning(f"⚠️ Some entries in '{col}' column are empty. This might affect the analysis.")
        
        
        return df[required_columns]
    except Exception as e:
        st.error(f"Error processing file: {str(e)}")
        return None

def preprocess_text(text):
    """Preprocess text to add appropriate formatting before summarization"""
    if not isinstance(text, str) or not text.strip():
        return text
        
    # Split text into sentences (basic implementation)
    sentences = [s.strip() for s in text.replace('. ', '.\n').split('\n')]
    
    # Remove empty sentences
    sentences = [s for s in sentences if s]
    
    # Join with proper line breaks
    formatted_text = '\n'.join(sentences)
    
    return formatted_text

def post_process_summary(summary):
    """Clean up and improve summary coherence"""
    if not summary:
        return summary
        
    # Split into sentences
    sentences = [s.strip() for s in summary.split('.')]
    sentences = [s for s in sentences if s]  # Remove empty sentences
    
    # Fix common issues
    processed_sentences = []
    for i, sentence in enumerate(sentences):
        # Remove redundant words/phrases
        sentence = sentence.replace(" and and ", " and ")
        sentence = sentence.replace("appointment and appointment", "appointment")
        
        # Fix common grammatical issues
        sentence = sentence.replace("Cancers distress", "Cancer distress")
        sentence = sentence.replace("  ", " ")  # Remove double spaces
        
        # Capitalize first letter of each sentence
        sentence = sentence.capitalize()
        
        # Add to processed sentences if not empty
        if sentence.strip():
            processed_sentences.append(sentence)
    
    # Join sentences with proper spacing and punctuation
    cleaned_summary = '. '.join(processed_sentences)
    if cleaned_summary and not cleaned_summary.endswith('.'):
        cleaned_summary += '.'
        
    return cleaned_summary

def improve_summary_generation(text, model, tokenizer):
    """Generate improved summary with better prompt and validation"""
    if not isinstance(text, str) or not text.strip():
        return "No abstract available to summarize."
    
    # Add a more specific prompt
    formatted_text = (
        "Summarize this medical research paper following this structure exactly:\n"
        "1. Background and objectives\n"
        "2. Methods\n"
        "3. Key findings with specific numbers/percentages\n"
        "4. Main conclusions\n"
        "Original text: " + preprocess_text(text)
    )
    
    # Adjust generation parameters
    inputs = tokenizer(formatted_text, return_tensors="pt", max_length=1024, truncation=True)
    inputs = {k: v.to(model.device) for k, v in inputs.items()}
    
    with torch.no_grad():
        summary_ids = model.generate(
            **{
                "input_ids": inputs["input_ids"],
                "attention_mask": inputs["attention_mask"],
                "max_length": 200,
                "min_length": 50,
                "num_beams": 5,
                "length_penalty": 1.5,
                "no_repeat_ngram_size": 3,
                "temperature": 0.7,
                "repetition_penalty": 1.5
            }
        )
    
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    
    # Post-process the summary
    processed_summary = post_process_summary(summary)
    
    # Validate the summary
    if not validate_summary(processed_summary, text):
        # If validation fails, try one more time with different parameters
        with torch.no_grad():
            summary_ids = model.generate(
                **{
                    "input_ids": inputs["input_ids"],
                    "attention_mask": inputs["attention_mask"],
                    "max_length": 200,
                    "min_length": 50,
                    "num_beams": 4,
                    "length_penalty": 2.0,
                    "no_repeat_ngram_size": 4,
                    "temperature": 0.8,
                    "repetition_penalty": 2.0
                }
            )
        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
        processed_summary = post_process_summary(summary)
    
    return processed_summary

def validate_summary(summary, original_text):
    """Validate summary content against original text"""
    # Check for age inconsistencies
    age_mentions = re.findall(r'(\d+\.?\d*)\s*years?', summary.lower())
    if len(age_mentions) > 1:  # Multiple age mentions
        return False
    
    # Check for repetitive sentences
    sentences = summary.split('.')
    unique_sentences = set(s.strip().lower() for s in sentences if s.strip())
    if len(sentences) - len(unique_sentences) > 1:  # More than one duplicate
        return False
    
    # Check summary isn't too long or too short compared to original
    summary_words = len(summary.split())
    original_words = len(original_text.split())
    if summary_words < 20 or summary_words > original_words * 0.8:
        return False
    
    return True

def generate_focused_summary(question, abstracts, model, tokenizer):
    """Generate focused summary based on question"""
    # Preprocess each abstract
    formatted_abstracts = [preprocess_text(abstract) for abstract in abstracts]
    combined_input = f"Question: {question} Abstracts: " + " [SEP] ".join(formatted_abstracts)
    
    inputs = tokenizer(combined_input, return_tensors="pt", max_length=1024, truncation=True)
    inputs = {k: v.to(model.device) for k, v in inputs.items()}
    
    with torch.no_grad():
        summary_ids = model.generate(
            **{
                "input_ids": inputs["input_ids"],
                "attention_mask": inputs["attention_mask"],
                "max_length": 200,
                "min_length": 50,
                "num_beams": 4,
                "length_penalty": 2.0,
                "early_stopping": True
            }
        )
    
    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)

def create_filter_controls(df, sort_column):
    """Create appropriate filter controls based on the selected column"""
    filtered_df = df.copy()
    
    if sort_column == 'Publication Year':
        # Year range slider
        year_min = int(df['Publication Year'].min())
        year_max = int(df['Publication Year'].max())
        col1, col2 = st.columns(2)
        with col1:
            start_year = st.number_input('From Year', 
                min_value=year_min, 
                max_value=year_max,
                value=year_min)
        with col2:
            end_year = st.number_input('To Year', 
                min_value=year_min, 
                max_value=year_max,
                value=year_max)
        filtered_df = filtered_df[
            (filtered_df['Publication Year'] >= start_year) & 
            (filtered_df['Publication Year'] <= end_year)
        ]
        
    elif sort_column == 'Authors':
        # Multi-select for authors
        unique_authors = sorted(set(
            author.strip()
            for authors in df['Authors'].dropna()
            for author in authors.split(';')
        ))
        selected_authors = st.multiselect(
            'Select Authors',
            unique_authors
        )
        if selected_authors:
            filtered_df = filtered_df[
                filtered_df['Authors'].apply(
                    lambda x: any(author in str(x) for author in selected_authors)
                )
            ]
            
    elif sort_column == 'Source Title':
        # Multi-select for source titles
        unique_sources = sorted(df['Source Title'].unique())
        selected_sources = st.multiselect(
            'Select Sources',
            unique_sources
        )
        if selected_sources:
            filtered_df = filtered_df[filtered_df['Source Title'].isin(selected_sources)]
            
    elif sort_column == 'Article Title':
        # Only alphabetical sorting, no filtering
        pass

    
    return filtered_df

def main():
    st.title("🔬 Biomedical Papers Analysis")

    st.info("""
    **📋 File Upload Requirements:**
    - Excel file (.xlsx or .xls) with **maximum 5 papers**
    - Must contain these columns:
      • Abstract
      • Article Title
      • Authors
      • Source Title
      • Publication Year
      • DOI
      • Times Cited, All Databases
    """)

    
    # File upload section
    uploaded_file = st.file_uploader(
        "Upload Excel file containing papers (max 5 papers)", 
        type=['xlsx', 'xls'],
        help="File must contain: Abstract, Article Title, Authors, Source Title, Publication Year, DOI"
    )
    
    # Question input - moved up but hidden initially
    question_container = st.empty()
    question = ""
    
    if uploaded_file is not None:
    # Process Excel file
        if st.session_state.processed_data is None:
            with st.spinner("Processing file..."):
                df = process_excel(uploaded_file)
                if df is not None:
                    df = df.dropna(subset=["Abstract"])
                    if len(df) > 0:
                        st.session_state.processed_data = df
                        st.success(f"✅ Successfully loaded {len(df)} papers with abstracts")
                    else:
                        st.error("❌ No valid papers found after processing. Please check your file.")
        
        if st.session_state.processed_data is not None:
            df = st.session_state.processed_data
            st.write(f"📊 Loaded {len(df)} papers with abstracts")
            
            # Get question before processing
            with question_container:
                question = st.text_input(
                    "Enter your research question (optional):",
                    help="If provided, a question-focused summary will be generated after individual summaries"
                )
            
            # Single button for both processes
            if not st.session_state.get('processing_started', False):
                if st.button("Start Analysis"):
                    st.session_state.processing_started = True
            
            # Show processing status and results
            if st.session_state.get('processing_started', False):
                # Individual Summaries Section
                st.header("📝 Individual Paper Summaries")
                
                # Generate summaries if not already done
                if st.session_state.summaries is None:
                    try:
                        with st.spinner("Generating individual paper summaries..."):
                            model, tokenizer = load_model("summarize")
                            summaries = []
                            progress_bar = st.progress(0)
                            
                            for idx, abstract in enumerate(df['Abstract']):
                                summary = improve_summary_generation(abstract, model, tokenizer)
                                summaries.append(summary)
                                progress_bar.progress((idx + 1) / len(df))
                            
                            st.session_state.summaries = summaries
                            cleanup_model(model, tokenizer)
                            progress_bar.empty()
                            
                    except Exception as e:
                        st.error(f"Error generating summaries: {str(e)}")
                        st.session_state.processing_started = False
                
                # Display summaries with improved sorting and filtering
                if st.session_state.summaries is not None:
                    col1, col2 = st.columns(2)
                    with col1:
                        sort_options = ['Article Title', 'Authors', 'Publication Year', 'Source Title', 'Times Cited']
                        sort_column = st.selectbox("Sort/Filter by:", sort_options)
                    with col2:
                        if sort_column == 'Article Title':
                            ascending = st.radio(
                                "Sort order",
                                ["A to Z", "Z to A"],
                                horizontal=True
                            ) == "A to Z"
                        elif sort_column == 'Times Cited':
                            ascending = st.radio(
                                "Sort order",
                                ["Most cited first", "Least cited first"],
                                horizontal=True
                            ) == "Least cited first"
                        else:
                            ascending = True  # Default for other columns
                    
                    # Create display dataframe
                    display_df = df.copy()
                    display_df['Summary'] = st.session_state.summaries
                    display_df['Publication Year'] = display_df['Publication Year'].astype(int)
                    display_df.rename(columns={'Times Cited, All Databases': 'Times Cited'}, inplace=True)
                    display_df['Times Cited'] = display_df['Times Cited'].fillna(0).astype(int)
                    
                    # Apply filters
                    filtered_df = create_filter_controls(display_df, sort_column)
                    
                    # Apply sorting
                    if sort_column == 'Times Cited':
                        sorted_df = filtered_df.sort_values(by=sort_column, ascending=ascending)
                    elif sort_column == 'Article Title':
                        sorted_df = filtered_df.sort_values(by=sort_column, ascending=ascending)
                    else:
                        sorted_df = filtered_df
                    
                    # Show number of filtered results
                    if len(sorted_df) != len(display_df):
                        st.write(f"Showing {len(sorted_df)} of {len(display_df)} papers")
                    
                    # Apply custom styling
                    st.markdown("""
                    <style>
                    .paper-info {
                        border: 1px solid #ddd;
                        padding: 15px;
                        margin-bottom: 20px;
                        border-radius: 5px;
                    }
                    .paper-section {
                        margin-bottom: 10px;
                    }
                    .section-header {
                        font-weight: bold;
                        color: #555;
                        margin-bottom: 8px;
                    }
                    .paper-title {
                        margin-top: 5px;
                        margin-bottom: 10px;
                    }
                    .paper-meta {
                        font-size: 0.9em;
                        color: #666;
                    }
                    .doi-link {
                        color: #0366d6;
                    }
                    </style>
                    """, unsafe_allow_html=True)
                    
                    # Display papers using the filtered and sorted dataframe
                    for _, row in sorted_df.iterrows():
                        paper_info_cols = st.columns([1, 1])
                        
                        with paper_info_cols[0]:  # PAPER column
                            st.markdown('<div class="paper-section"><div class="section-header">PAPER</div>', unsafe_allow_html=True)
                            st.markdown(f"""
                            <div class="paper-info">
                                <div class="paper-title">{row['Article Title']}</div>
                                <div class="paper-meta">
                                    <strong>Authors:</strong> {row['Authors']}<br>
                                    <strong>Source:</strong> {row['Source Title']}<br>
                                    <strong>Publication Year:</strong> {row['Publication Year']}<br>
                                    <strong>Times Cited:</strong> {row['Times Cited']}<br>
                                    <strong>DOI:</strong> {row['DOI'] if pd.notna(row['DOI']) else 'None'}
                                </div>
                            </div>
                            """, unsafe_allow_html=True)

                        with paper_info_cols[1]:  # SUMMARY column
                            st.markdown('<div class="paper-section"><div class="section-header">SUMMARY</div>', unsafe_allow_html=True)
                            st.markdown(f"""
                            <div class="paper-info">
                                {row['Summary']}
                            </div>
                            """, unsafe_allow_html=True)
                        
                        # Add spacing between papers
                        st.markdown("<div style='margin-bottom: 20px;'></div>", unsafe_allow_html=True)
                
                # Question-focused Summary Section (only if question provided)
                if question.strip():
                    st.header("❓ Question-focused Summary")
                    
                    if not st.session_state.get('focused_summary_generated', False):
                        try:
                            with st.spinner("Analyzing relevant papers..."):
                                # Initialize text processor if needed
                                if st.session_state.text_processor is None:
                                    st.session_state.text_processor = TextProcessor()
                                
                                # Find relevant abstracts
                                results = st.session_state.text_processor.find_most_relevant_abstracts(
                                    question,
                                    df['Abstract'].tolist(),
                                    top_k=5
                                )
                                
                                # Load question-focused model
                                model, tokenizer = load_model("question_focused")
                                
                                # Generate focused summary
                                relevant_abstracts = df['Abstract'].iloc[results['top_indices']].tolist()
                                focused_summary = generate_focused_summary(
                                    question,
                                    relevant_abstracts,
                                    model,
                                    tokenizer
                                )
                                
                                # Store results
                                st.session_state.focused_summary = focused_summary
                                st.session_state.relevant_papers = df.iloc[results['top_indices']]
                                st.session_state.relevance_scores = results['scores']
                                st.session_state.focused_summary_generated = True
                                
                                # Cleanup second model
                                cleanup_model(model, tokenizer)
                        
                        except Exception as e:
                            st.error(f"Error generating focused summary: {str(e)}")
                    
                    # Display focused summary results
                    if st.session_state.get('focused_summary_generated', False):
                        st.subheader("Summary")
                        st.write(st.session_state.focused_summary)
                        
                        st.subheader("Most Relevant Papers")
                        relevant_papers = st.session_state.relevant_papers[
                            ['Article Title', 'Authors', 'Publication Year', 'DOI']
                        ].copy()
                        relevant_papers['Relevance Score'] = st.session_state.relevance_scores
                        relevant_papers['Publication Year'] = relevant_papers['Publication Year'].astype(int)
                        st.dataframe(relevant_papers, hide_index=True)

if __name__ == "__main__":
    main()