app.py

import gradio as gr
from transformers import pipeline
import PyPDF2
import markdown
import matplotlib.pyplot as plt
import io
import base64
import torch
from fpdf import FPDF
import os
import tempfile
import glob

# Preload models
models = {
    "distilbert-base-uncased-distilled-squad": "distilbert-base-uncased-distilled-squad",
    "roberta-base-squad2": "deepset/roberta-base-squad2",
    "bert-large-uncased-whole-word-masking-finetuned-squad": "bert-large-uncased-whole-word-masking-finetuned-squad",
    "albert-base-v2": "twmkn9/albert-base-v2-squad2",
    "xlm-roberta-large-squad2": "deepset/xlm-roberta-large-squad2"
}

loaded_models = {}

# Ensure we're using the CPU if GPU isn't available or necessary
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def load_model(model_name):
    if model_name not in loaded_models:
        loaded_models[model_name] = pipeline("question-answering", model=models[model_name], device=0 if torch.cuda.is_available() else -1)
    return loaded_models[model_name]
    
def generate_score_chart(score):
    plt.figure(figsize=(6, 4))
    plt.bar(["Confidence Score"], [score], color='skyblue')
    plt.ylim(0, 1)
    plt.ylabel("Score")
    plt.title("Confidence Score")
    
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    plt.close()
    buf.seek(0)
    return base64.b64encode(buf.getvalue()).decode()

def highlight_relevant_text(context, start, end):
    highlighted_text = (
        context[:start] + 
        '<mark style="background-color: yellow;">' + 
        context[start:end] + 
        '</mark>' + 
        context[end:]
    )
    return highlighted_text

def answer_question(model_name, file, question, status):
    status = "Loading model..."
    model = load_model(model_name)
    
    if file is not None:
        file_name = file.name
        if file_name.endswith(".pdf"):
            pdf_reader = PyPDF2.PdfReader(file)
            context = ""
            for page_num in range(len(pdf_reader.pages)):
                context += pdf_reader.pages[page_num].extract_text()
        elif file_name.endswith(".md"):
            context = file.read().decode('utf-8')
            context = markdown.markdown(context)
        else:
            context = file.read().decode('utf-8')
    else:
        context = ""
    
    result = model(question=question, context=context)
    answer = result['answer']
    score = result['score']
    start = result['start']
    end = result['end']
    
    # Highlight relevant text
    highlighted_context = highlight_relevant_text(context, start, end)
    
    # Generate the score chart
    score_chart = generate_score_chart(score)
    
    # Explain score
    score_explanation = f"The confidence score ranges from 0 to 1, where a higher score indicates higher confidence in the answer's correctness. In this case, the score is {score:.2f}. A score closer to 1 implies the model is very confident about the answer."
    
    # Generate the PDF report
    pdf_report = generate_pdf_report(question, answer, f"{score:.2f}", score_explanation, score_chart, highlighted_context)
    
    status = "Model loaded"
    return highlighted_context, f"{score:.2f}", score_explanation, score_chart, pdf_report, status

# Define the Gradio interface
with gr.Blocks() as interface:
    gr.Markdown(
        """
        # Question Answering System
        Upload a document (text, PDF, or Markdown) and ask questions to get answers based on the context.
        
        **Supported File Types**: `.txt`, `.pdf`, `.md`
        """)
    
    with gr.Row():
        model_dropdown = gr.Dropdown(
            choices=list(models.keys()),
            label="Select Model",
            value="distilbert-base-uncased-distilled-squad"
        )
    
    with gr.Row():
        file_input = gr.File(label="Upload Document", file_types=["text", "pdf", "markdown"])
        question_input = gr.Textbox(lines=2, placeholder="Enter your question here...", label="Question")
    
    with gr.Row():
        answer_output = gr.HTML(label="Highlighted Answer")
        score_output = gr.Textbox(label="Confidence Score")
        explanation_output = gr.Textbox(label="Score Explanation")
        chart_output = gr.Image(label="Score Chart")
        pdf_output = gr.File(label="Download PDF Report")
    
    with gr.Row():
        submit_button = gr.Button("Submit")
    
    status_output = gr.Markdown(value="")

    def on_submit(model_name, file, question):
        return answer_question(model_name, file, question, status="Loading model...")

    submit_button.click(
        on_submit,
        inputs=[model_dropdown, file_input, question_input],
        outputs=[answer_output, score_output, explanation_output, chart_output, pdf_output, status_output]
    )

if __name__ == "__main__":
    interface.launch(share=True)