app.py

import gradio as gr
from transformers import pipeline

# Load the large language model (LLM)
try:
    print("Loading the language model...")
    llm_pipeline = pipeline("text-generation", model="Llama-3.2-11B-Vision-Instruct")  # You can use a different model here
    print("Model loaded successfully!")
except Exception as e:
    print(f"Error loading model: {e}")
    llm_pipeline = None

# Define the function to generate text based on input prompt
def generate_text(prompt):
    if llm_pipeline is None:
        return "Error: Model not loaded."
    result = llm_pipeline(prompt, max_length=100, num_return_sequences=1)
    return result[0]['generated_text']

# Create the Gradio interface
interface = gr.Interface(
    fn=generate_text,
    inputs=gr.Textbox(lines=7, label="Input Prompt"),
    outputs="text",
    title="Large Language Model Text Generation",
    description="Enter a prompt to generate text using a large language model."
)

print("Launching the Gradio interface...")
# Launch the interface
interface.launch()