app.py

import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import gradio as gr

# Load the fine-tuned model and tokenizer from Hugging Face
model_name = "Rehman1603/Travel_fine_tuned_gpt2_model_final"  # Replace with your Hugging Face model path
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# Move model to the appropriate device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Function to generate responses
def generate_response(question, max_length=1024, temperature=0.5, top_k=50, top_p=0.95):
    input_text = f"Question: {question} Answer:"
    input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)
    
    # Generate response
    output = model.generate(
        input_ids,
        max_length=max_length,
        temperature=temperature,
        #num_return_sequences=1,
        #no_repeat_ngram_size=2,
        top_k=top_k,
        top_p=top_p,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    
    # Decode and return the response
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return response.split("Answer:")[-1].strip()

# Gradio Interface
def chat_interface(question, history):
    response = generate_response(question)
    history = history or []
    history.append((question, response))
    return history, history

# Test questions
test_questions = [
    "Hi",
    "What is the duration of the Economy Umrah Package?",
    "Good morning",
    "What is the distance of hotels from Haram in Package 4?",
    "What is the price of a 14-night Umrah package with air tickets in Package 1",
    "What is the price of a 20-night Umrah package without air tickets?"
]

# Create Gradio interface
with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    state = gr.State()
    with gr.Row():
        txt = gr.Textbox(show_label=False, placeholder="Enter your question here")
    with gr.Row():
        submit_btn = gr.Button("Submit")
        clear_btn = gr.Button("Clear")
    
    # Example questions
    examples = gr.Examples(examples=test_questions, inputs=txt)
    
    # Event handling
    submit_btn.click(chat_interface, [txt, state], [chatbot, state])
    clear_btn.click(lambda: None, None, chatbot, queue=False)

# Launch the interface
demo.launch(debug=True)