app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load your text generation model from Hugging Face using its identifier
model_identifier = "your-model-name-on-hugging-face"
model = AutoModelForCausalLM.from_pretrained(model_identifier)
tokenizer = AutoTokenizer.from_pretrained(model_identifier)

def generate_response(input_prompt):
    # Tokenize input prompt
    input_ids = tokenizer.encode(input_prompt, return_tensors="pt", max_length=512, truncation=True)
    
    # Generate response
    output_ids = model.generate(input_ids, max_length=100, num_return_sequences=1)
    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    
    return response

# Create Gradio interface
input_prompt = gr.inputs.Textbox(lines=5, label="Input Prompt")
output_text = gr.outputs.Textbox(label="Response")

gr.Interface(
    generate_response,
    inputs=input_prompt,
    outputs=output_text,
    title="OmniCode",
    description="Multi programming coding assistant",
    theme="compact"
).launch()