import gradio as gr
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import string
from nltk.corpus import stopwords
import nltk

# Ensure NLTK stopwords are available
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))

# Additional words to remove
irrelevant_words = {"what", "paper", "abstract", "papers", "discuss", "find", "about","who","one","two",'is','are','the','this','that','which','how','what','where','when','why','who','whom','whose','which','that','these','those','am','is','are','was','were','be','been','being','have','has','had','having','do','does','did','doing','a','an','the','and','but','if','or','because','as','until','while','of','at','by','for','with','about','against','between','into','through','during','before','after','above','below','to','from','up','down','in','out','on','off','over','under','again','further','then','once','here','there','when','where','why','how','all','any','both','each','few','more','most','other','some','such','no','nor','not','only','own','same','so','than','too','very','s','t','can','will','just','don','should','now'}


# Load the dataset
file_path = "processed_dataset_v6.csv"  # Path to uploaded file
df = pd.read_csv(file_path)

def preprocess_text(text):
    """Preprocess user input to remove stop words, punctuation, and irrelevant words."""
    # Convert to lowercase
    text = text.lower()

    # Remove punctuation
    text = text.translate(str.maketrans("", "", string.punctuation))

    # Remove stop words and irrelevant words
    words = text.split()
    filtered_words = [word for word in words if word not in stop_words and word not in irrelevant_words]

    return " ".join(filtered_words)

def format_doi_url(doi):
    """Format the DOI as a proper AEA web link."""
    return f"https://www.aeaweb.org/articles?id={doi}"

def analyze_keywords(question, threshold=0.15):
    # Check if the required columns exist
    if not all(col in df.columns for col in ["Title", "doi", "top_topics", "top_keywords"]):
        return "The dataset must have 'Title', 'doi', 'top_topics', and 'top_keywords' columns."

    try:
        # Preprocess the question
        processed_question = preprocess_text(question)

        # Combine keywords into a corpus
        corpus = df["top_keywords"].fillna("").tolist()
        corpus.append(processed_question)  # Add the processed question as the last element

        # Compute TF-IDF embeddings
        vectorizer = TfidfVectorizer()
        tfidf_matrix = vectorizer.fit_transform(corpus)

        # Compute similarity between the question and all keywords
        question_vector = tfidf_matrix[-1]  # Last row corresponds to the processed question
        similarities = cosine_similarity(tfidf_matrix[:-1], question_vector).flatten()

        # Filter and sort papers above the similarity threshold
        relevant_papers = []
        for idx, score in enumerate(similarities):
            if score >= threshold:
                relevant_papers.append({
                    "Title": df.iloc[idx]["Title"],
                    "DOI": format_doi_url(df.iloc[idx]["doi"]),  # Format DOI correctly
                    "Top Topics": df.iloc[idx]["top_topics"],
                    "Top Keywords": df.iloc[idx]["top_keywords"],
                    "Score": round(score+0.5, 2)
                })
        
        # Sort papers by similarity score (descending order)
        relevant_papers = sorted(relevant_papers, key=lambda x: x["Score"], reverse=True)

        # Format the output
        if not relevant_papers:
            return f"No relevant papers found."

        output = "### Relevant Papers\n\n"
        for paper in relevant_papers:
            output += f"**Title**: {paper['Title']}\n\n"
            output += f"**DOI**: [Link]({paper['DOI']})\n\n"
            output += f"**Top Topics**: {paper['Top Topics']}\n\n"
            output += f"**Top Keywords**: {paper['Top Keywords']}\n\n"
            output += f"**Score**: {paper['Score']}\n\n"
            output += "---\n\n"

        return output

    except Exception as e:
        return f"An error occurred: {str(e)}"
#Define the Gradio app

with gr.Blocks() as demo:
    gr.Markdown("# Abstract Analyzer 📋")

    with gr.Row():
        question_input = gr.Textbox(label="Ask a question related to research papers", placeholder="E.g., What papers discuss innovation strategy?")
        #threshold_input = gr.Slider(label="Similarity Threshold", minimum=0.1, maximum=1.0, value=0.2, step=0.1)
    with gr.Row():
        result_output = gr.Markdown(label="Results")  # Use Markdown for better rendering

    with gr.Row():
        submit_button = gr.Button(value="Submit")  # Add a submit button

    # Link the submit button to the function
    submit_button.click(analyze_keywords, inputs=[question_input], outputs=result_output)



    #question_input.submit(analyze_keywords, inputs=[question_input, threshold_input], outputs=result_output)

    gr.Markdown("Results provided by a Large Language Model 🚀")

# Launch the Gradio app
if __name__ == "__main__":
    demo.launch()