import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import gradio as gr
from threading import Thread
import os
from gradio_modal import Modal

checkpoint = "WillHeld/soft-raccoon"
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

@spaces.GPU(duration=120)
def predict(message, history, temperature, top_p):
    history.append({"role": "user", "content": message})
    input_text = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
    
    # Create a streamer
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    
    # Set up generation parameters
    generation_kwargs = {
        "input_ids": inputs,
        "max_new_tokens": 1024,
        "temperature": float(temperature),
        "top_p": float(top_p),
        "do_sample": True,
        "streamer": streamer,
        "eos_token_id": 128009,
    }
    
    # Run generation in a separate thread
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    
    # Yield from the streamer as tokens are generated
    partial_text = ""
    for new_text in streamer:
        partial_text += new_text
        yield partial_text

# Function to handle the report submission
def submit_report(satisfaction, feedback_text):
    # In a real application, you might save this to a database or file
    print(f"Report submitted - Satisfaction: {satisfaction}, Feedback: {feedback_text}")
    return "Thank you for your feedback! Your report has been submitted."

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=3):
            chatbot = gr.ChatInterface(
                predict,
                additional_inputs=[
                    gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature"),
                    gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-P")
                ],
                type="messages"
            )
        
        with gr.Column(scale=1):
            report_button = gr.Button("File a Report", variant="primary")
    
    # Create the modal with feedback form components
    with Modal(visible=False) as feedback_modal:
        with gr.Column():
            gr.Markdown("## We value your feedback!")
            gr.Markdown("Please tell us about your experience with the model.")
            
            satisfaction = gr.Radio(
                ["Very satisfied", "Satisfied", "Neutral", "Unsatisfied", "Very unsatisfied"],
                label="How satisfied are you with the model's responses?",
                value="Neutral"
            )
            
            feedback_text = gr.Textbox(
                lines=5,
                label="Please provide any additional feedback or describe issues you encountered:",
                placeholder="Enter your detailed feedback here..."
            )
            
            submit_button = gr.Button("Submit Feedback", variant="primary")
            response_text = gr.Textbox(label="Status", interactive=False)
    
    # Connect the "File a Report" button to show the modal
    report_button.click(
        lambda: Modal(visible=True),
        None,
        feedback_modal
    )
    
    # Connect the submit button to the submit_report function
    submit_button.click(
        submit_report,
        inputs=[satisfaction, feedback_text],
        outputs=response_text
    )

demo.launch()