app.py

import os
os.system("pip install transformers")
os.system("pip install torch")
os.system("pip install -U gradio")

import gradio as gr
from transformers import pipeline

# Use a pipeline as a high-level helper
pipe = pipeline("text-generation", model="MAsad789565/GPT2-medium-Finetuned")

def generate_text(prompt):
    # Generate text using the pipeline
    generated_text = pipe(prompt, max_length=100, temperature=1, top_k=20, top_p=0.9, do_sample=False)[0]['generated_text']
    return generated_text

# Function to be called when the button is clicked
def send_to_model(textbox_value):
    # Call the generate_text function with the provided input
    generated_text = generate_text(textbox_value)
    # Update the output Textbox with the generated text
    iface.set_inputs({"text": textbox_value})
    iface.set_outputs({"text": generated_text})

# Create a Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=gr.Textbox("text", placeholder="Enter your prompt here..."),
    outputs=gr.Textbox("text", placeholder=" Generated text will appear here..."),
    live=True,
    title="GPT-2 Medium Fine-tuned Model",
    description="Generate text using a fine-tuned GPT-2 medium model.",
    examples=[
        ["User: What is SSL certificate?"],
        ["User: Explain the concept of artificial intelligence."],
        ["User: Write python code to find factorial of a number."],
        
    ],
    theme="compact",
    # Add a button to send text to the model
    button="Generate",
    button_click=send_to_model  # Specify the function to be called when the button is clicked
)

# Launch the Gradio interface
iface.launch(share=True)