import gradio as gr
import transformers

# Load your custom models (example)
model_name = "microsoft/Phi-3-mini-4k-instruct"  # Replace with your model name
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = transformers.AutoModelForCausalLM.from_pretrained(model_name)

def chatbot_response(user_input):
    inputs = tokenizer.encode(user_input, return_tensors="pt")
    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

def upload_readme(filepath):
    if filepath is not None:
        with open(filepath, 'r', encoding='utf-8') as file:
            content = file.read()
        return content
    return "No file uploaded"

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            gr.Markdown("# Chatbot Interface")
            gr.Markdown("Upload your README file and interact with the chatbot.")
            
            # File upload
            readme_file = gr.File(label="Upload README file", type="filepath", file_types=[".md"])
            readme_content = gr.Textbox(label="README Content", lines=10, placeholder="README content will appear here...")
            
            # Display README content after upload
            readme_file.change(upload_readme, inputs=readme_file, outputs=readme_content)
            
            # Chatbot input and output
            user_input = gr.Textbox(label="Your message", placeholder="Type your message here...")
            output = gr.Textbox(label="Chatbot response", placeholder="Chatbot response will appear here...", lines=5)
            
            # Get chatbot response
            user_input.submit(chatbot_response, inputs=user_input, outputs=output)

demo.launch()