app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import login
import os

# Retrieve the Hugging Face token from the Space secrets
token = os.getenv("HF_TOKEN")

# Log in using the token
login(token=token)


# Load model and tokenizer
model_name = "nikunjcepatel/gpt_finetune_test"  #"meta-llama/Llama-3.2-3B"  # Replace with the correct model name if necessary
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define inference function
def generate_text(input_text):
    inputs = tokenizer(input_text, return_tensors="pt")
    #outputs = model.generate(inputs["input_ids"], max_length=256, num_return_sequences=1)
    outputs = model.generate(
    inputs["input_ids"],
    max_length=256,            # Set max length for output
    num_return_sequences=1,
    temperature=0.2,           # Control randomness (higher is more random)
    top_k=50,                  # Top-k sampling to limit vocabulary to top 50 choices
    top_p=0.8,                 # Nucleus sampling to choose tokens with 95% cumulative probability
    repetition_penalty=1.2,    # Penalize repetition; increase if repetitions persist
    do_sample=True             # Enable sampling for non-deterministic output
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

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

# Launch the interface
iface.launch()