import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import gradio as gr
from threading import Thread
import os
import json
import uuid
from datasets import Dataset
from huggingface_hub import HfApi, login
import time

# Install required packages if not present
from gradio_modal import Modal
import huggingface_hub
import datasets

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

# Constants for dataset
DATASET_REPO = "WillHeld/model-feedback"  # Replace with your username
DATASET_PATH = "./feedback_data"  # Local path to store feedback
DATASET_FILENAME = "feedback.jsonl"  # Filename for feedback data

# Ensure feedback directory exists
os.makedirs(DATASET_PATH, exist_ok=True)

# Feedback storage functions
def save_feedback_locally(conversation, satisfaction, feedback_text):
    """Save feedback to a local JSONL file"""
    # Create a unique ID for this feedback entry
    feedback_id = str(uuid.uuid4())
    
    # Create a timestamp
    timestamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
    
    # Prepare the feedback data
    feedback_data = {
        "id": feedback_id,
        "timestamp": timestamp,
        "conversation": conversation,
        "satisfaction": satisfaction,
        "feedback": feedback_text
    }
    
    # Save to local file
    feedback_file = os.path.join(DATASET_PATH, DATASET_FILENAME)
    with open(feedback_file, "a") as f:
        f.write(json.dumps(feedback_data) + "\n")
    
    return feedback_id

def push_feedback_to_hub(hf_token=None):
    """Push the local feedback data to HuggingFace as a dataset"""
    # Check if we have a token
    if hf_token is None:
        # Try to get token from environment variable
        hf_token = os.environ.get("HF_TOKEN")
        if hf_token is None:
            print("No HuggingFace token provided. Cannot push to Hub.")
            return False
    
    try:
        # Login to HuggingFace
        login(token=hf_token)
        
        # Check if we have data to push
        feedback_file = os.path.join(DATASET_PATH, DATASET_FILENAME)
        if not os.path.exists(feedback_file):
            print("No feedback data to push.")
            return False
        
        # Load data from the JSONL file
        with open(feedback_file, "r") as f:
            feedback_data = [json.loads(line) for line in f]
        
        # Create a dataset from the feedback data
        dataset = Dataset.from_list(feedback_data)
        
        # Push to Hub
        dataset.push_to_hub(
            DATASET_REPO,
            private=True  # Set to False if you want the dataset to be public
        )
        
        print(f"Feedback data pushed to {DATASET_REPO} successfully.")
        return True
    
    except Exception as e:
        print(f"Error pushing feedback data to Hub: {e}")
        return False

# Modified predict function to update conversation state
@spaces.GPU(duration=120)
def predict(message, history, temperature, top_p):
    # Update history with user message
    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,
    }
    
    # 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
    
    # After full generation, update state with assistant's response
    history.append({"role": "assistant", "content": partial_text})
    return partial_text

# Function to handle the research feedback submission
def submit_research_feedback(conversation_state, satisfaction, feedback_text):
    """Save user feedback both locally and to HuggingFace Hub"""
    # Save locally first
    feedback_id = save_feedback_locally(conversation_state, satisfaction, feedback_text)
    
    # Get token from environment variable
    env_token = os.environ.get("HF_TOKEN")
    
    # Use environment token
    push_success = push_feedback_to_hub(env_token)
    
    if push_success:
        status_msg = "Thank you for your valuable feedback! Your insights have been saved to the dataset."
    else:
        status_msg = "Thank you for your feedback! It has been saved locally, but couldn't be pushed to the dataset. Please check server logs."
    
    return status_msg

# Create the Gradio blocks interface
with gr.Blocks() as demo:
    # State to track conversation history
    conversation_state = gr.State([])
    
    with gr.Row():
        with gr.Column(scale=3):
            # Custom chat function wrapper to update state
            def chat_with_state(message, history, state, temperature, top_p):
                for partial_response in predict(message, history, temperature, top_p):
                    # Update our state with each yield
                    state = history.copy()
                    yield partial_response, state
                state = history.copy()
                print(state)
                return partial_response, state
            
            # Create ChatInterface
            chatbot = gr.ChatInterface(
                chat_with_state,
                additional_inputs=[
                    conversation_state,
                    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")
                ],
                additional_outputs=[conversation_state],
                type="messages"
            )
        
        with gr.Column(scale=1):
            report_button = gr.Button("Share Feedback", variant="primary")
    
    # Create the modal with feedback form components
    with Modal(visible=False) as feedback_modal:
        with gr.Column():
            gr.Markdown("## Research Preview Feedback")
            gr.Markdown("Thank you for testing our research model. Your feedback (positive or negative) helps us improve!")
            
            satisfaction = gr.Radio(
                ["Very satisfied", "Satisfied", "Neutral", "Unsatisfied", "Very unsatisfied"],
                label="How would you rate your experience with this research model?",
                value="Neutral"
            )
            
            feedback_text = gr.Textbox(
                lines=5,
                label="Share your observations (strengths, weaknesses, suggestions):",
                placeholder="We welcome both positive feedback and constructive criticism to help improve this research prototype..."
            )
            
            submit_button = gr.Button("Submit Research Feedback", variant="primary")
            response_text = gr.Textbox(label="Status", interactive=False)
    
    # Connect the "Share Feedback" button to show the modal
    report_button.click(
        lambda: Modal(visible=True),
        None,
        feedback_modal
    )
    
    # Connect the submit button to the submit_research_feedback function with the current conversation state
    submit_button.click(
        submit_research_feedback,
        inputs=[conversation_state, satisfaction, feedback_text],
        outputs=response_text
    )

# Launch the demo
demo.launch()