app.py

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

# Load the tokenizer from the Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained("adarsh3601/my_gemma3_pt")

# Load the model from Hugging Face Hub (Assuming you are using a transformer model here)
model = AutoModelForCausalLM.from_pretrained("adarsh3601/my_gemma3_pt")

# Function to generate response using the model
def generate_response(input_text):
    # Tokenize the input text
    inputs = tokenizer(input_text, return_tensors="pt")
    
    # Generate output using the model
    with torch.no_grad():  # Disable gradients for inference
        outputs = model.generate(inputs['input_ids'], max_length=50)  # You can adjust max_length and other parameters
    
    # Decode the output and return it
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Create a Gradio interface
def create_gradio_interface():
    # Interface with a text input and a text output
    interface = gr.Interface(
        fn=generate_response,  # Function to call for generation
        inputs=gr.Textbox(label="Enter Input Text"),  # Textbox for user input
        outputs=gr.Textbox(label="Generated Response"),  # Textbox for output text
        title="Text Generation with My Model",  # Title for the interface
        description="Enter some text to generate a response using the trained model."  # Description
    )
    return interface

# Launch the Gradio interface
if __name__ == "__main__":
    interface = create_gradio_interface()
    interface.launch()