app.py

import gradio as gr
from transformers import pipeline

# Load a more suitable model for conversational responses
model_name = "gpt2"  # You might want to try 'gpt-neo' or 'gpt-3.5-turbo' if available
generator = pipeline("text-generation", model=model_name)

# Inference function
def generate_response(prompt):
    # Generate text with a more structured approach
    response = generator(prompt, max_length=100, num_return_sequences=1)[0]['generated_text']
    return response.strip()  # Clean up any leading/trailing whitespace

# Gradio interface
interface = gr.Interface(
    fn=generate_response, 
    inputs="text", 
    outputs="text", 
    title="Conversational LLM",
    description="Enter a message to receive a relevant response."
)

# Launch the interface
interface.launch()