app.py

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

# Load model and tokenizer with the token from environment variables
model_name = "meta-llama/Llama-2-7b-hf"
token = os.getenv("HUGGINGFACE_TOKEN")  # Get token from environment

# Add print statements for debugging
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_name, token=token)
print("Tokenizer loaded.")

print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(model_name, token=token, torch_dtype=torch.float16)
print("Model loaded.")

model = model.to("cuda" if torch.cuda.is_available() else "cpu")
print("Model moved to device.")

# Function to generate responses
def generate_response(user_input, chat_history):
    chat_history.append({"role": "user", "content": user_input})
    conversation = ""
    for turn in chat_history:
        conversation += f"{turn['role']}: {turn['content']}\n"
    inputs = tokenizer(conversation, return_tensors="pt").to(model.device)
    outputs = model.generate(inputs.input_ids, max_length=500, do_sample=True, temperature=0.7)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    chat_history.append({"role": "assistant", "content": response})
    return response, chat_history

# Define Gradio chat interface
def chat_interface():
    chat_history = []
    def respond(user_input):
        response, chat_history = generate_response(user_input, chat_history)
        return response
    gr.Interface(fn=respond, inputs="text", outputs="text", title="LLaMA-2 Chatbot").launch()

# Call the interface function to start the app
print("Launching Gradio interface...")
chat_interface()