app.py

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

# Load our Tokenizer
tokenizer = AutoTokenizer.from_pretrained("nlux/CodeLlama-7b-hf_merge")

# Load your model
model = "nlux/CodeLlama-7b-hf_merge"

# load into pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

def predict(input):
    # Generate text using the pipeline
    outputs = pipe(input, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.pad_token_id)
    output = outputs[0]['generated_text'].strip()

    # Print results
    print(f"Generated Answer:\\n{output}")
    return output

# Create a Gradio interface
iface = gr.Interface(fn=predict, inputs="text", outputs="text")

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