app.py

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

# Specify your finetuned model name here
model_name = "BotCuddles/to_deploy"

# Load your finetuned model and tokenizer from the Hugging Face Hub
try:
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
    generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
    print("Model and tokenizer loaded successfully!")
except Exception as e:
    print(f"Error loading model and tokenizer: {e}")
    generator = None
top_k = 50
top_p = 0.9
temperature = 0.7
max_new_tokens = 120
def generate_text(prompt):
    if generator:
        try:
            full_prompt = "Understand the following message from user and give a compassionate reply. Message: " + prompt
            result = generator(
                full_prompt, 
                top_k=top_k, 
                top_p=top_p, 
                temperature=temperature, 
                max_new_tokens=max_new_tokens, 
                num_return_sequences=1
            )
            return result[0]['generated_text']
        except Exception as e:
            return f"Error generating text: {e}"
    else:
        return "Model not loaded properly."

iface = gr.Interface(
    fn=generate_text,
    inputs=
        gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
        
    outputs="text",
    title="Demo - Lift Me Up",
    description="Generate text for relief"
)

if __name__ == "__main__":
    iface.launch()