app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForQuestionAnswering

MODEL_NAME = "Salommee/bert-squad-qa"

print("Loading model...")
try:
    tokenizer = AutoTokenizer.from_pretrained(
        MODEL_NAME,
        trust_remote_code=True
    )
    model = AutoModelForQuestionAnswering.from_pretrained(
        MODEL_NAME,
        trust_remote_code=True
    )
    print("Model loaded successfully!")
except Exception as e:
    print(f"Error loading model: {e}")
    raise

# --- Function to answer questions ---
def answer_question(context, question):
    if not context.strip(): 
        return "❌ Provide context.", "N/A"
    if not question.strip(): 
        return "❌ Provide question.", "N/A"
    
    inputs = tokenizer(
        question,
        context,
        truncation="only_second",
        max_length=384,
        return_tensors="pt",
        padding=True
    )
    
    with torch.no_grad():
        outputs = model(**inputs)
    
    start_idx = torch.argmax(outputs.start_logits)
    end_idx = torch.argmax(outputs.end_logits)
    
    start_score = torch.softmax(outputs.start_logits, dim=1)[0][start_idx].item()
    end_score = torch.softmax(outputs.end_logits, dim=1)[0][end_idx].item()
    confidence = start_score * end_score
    
    if start_idx > end_idx or start_idx==0 or end_idx==0:
        return "❌ Answer not found. Try rephrasing your question.", f"{confidence:.2%}"
    
    answer = tokenizer.decode(inputs.input_ids[0][start_idx:end_idx+1], skip_special_tokens=True)
    emoji = "🟢" if confidence>0.8 else "🟡" if confidence>0.5 else "🔴"
    return f"✅ {answer}", f"{emoji} {confidence:.2%}"

# --- Example inputs ---
examples = [
    ["Paris is the capital of France.", "What is the capital of France?"],
    ["Eiffel Tower built 1887-1889.", "When was the Eiffel Tower built?"],
    ["Machine learning automates model building.", "What is machine learning?"]
]

# --- Build Gradio interface ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🤖 BERT Question Answering")
    
    with gr.Row():
        with gr.Column(scale=2):
            context_input = gr.Textbox(label="📝 Context", lines=6, placeholder="Enter context here...")
            question_input = gr.Textbox(label="❓ Question", lines=2, placeholder="Ask your question...")
            submit_btn = gr.Button("🔍 Get Answer")
        with gr.Column(scale=1):
            answer_output = gr.Textbox(label="💡 Answer", lines=2)
            confidence_output = gr.Textbox(label="📊 Confidence", lines=1)
    
    gr.Examples(
        examples,
        inputs=[context_input, question_input],
        outputs=[answer_output, confidence_output],
        fn=answer_question
    )
    
    submit_btn.click(
        fn=answer_question,
        inputs=[context_input, question_input],
        outputs=[answer_output, confidence_output]
    )

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