app.py

import gradio as gr
from transformers import LlamaForCausalLM, LlamaTokenizer
import torch
import os

# Define model name and local directory
local_model_dir = "./chameleon-7b"

# Verify that the directory contains necessary files
assert os.path.exists(local_model_dir), f"{local_model_dir} does not exist"

# Load the model and tokenizer from the local directory
tokenizer = LlamaTokenizer.from_pretrained(local_model_dir)
model = LlamaForCausalLM.from_pretrained(local_model_dir)

# Function to generate response
def generate_response(input_text):
    inputs = tokenizer(input_text, return_tensors="pt")
    with torch.no_grad():
        outputs = model.generate(inputs.input_ids, max_length=500, num_return_sequences=1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Custom CSS
css = """
body {
    background-color: #2e3440;
    color: #d8dee9;
    font-family: 'Roboto', sans-serif;
}
.gradio-container {
    background-color: #3b4252;
    border: 2px solid #88c0d0;
    border-radius: 10px;
    padding: 20px;
}
.gr-button {
    background-color: #5e81ac;
    color: #d8dee9;
    border-radius: 5px;
}
"""

# Define the Gradio interface
with gr.Blocks(css=css) as demo:
    gr.Markdown("# Chat with Chameleon-7B", elem_id="header")
    with gr.Row():
        with gr.Column():
            input_text = gr.Textbox(label="Your Input", placeholder="Type your message here...", lines=4)
            output_text = gr.Textbox(label="Chameleon-7B Response", placeholder="The model's response will appear here...", lines=4)
            submit_button = gr.Button("Submit")
        with gr.Column():
            gr.Markdown("### Instructions")
            gr.Markdown("Enter your message in the input box and press submit to get a response from Chameleon-7B.")
    submit_button.click(fn=generate_response, inputs=input_text, outputs=output_text)

# Launch the Gradio app
demo.launch()