import gradio as gr
import os
import time
import json
import requests
import threading

"""
This app uses the Hugging Face Inference API to generate responses from the
Trinoid/Data_Management_Mistral model.
"""
# Get token from environment
HF_TOKEN = os.environ.get("HF_TOKEN")
print(f"HF_TOKEN is {'available' if HF_TOKEN else 'not available'}")

# Setup API for the Hugging Face Inference API
MODEL_ID = "Trinoid/Data_Management_Mistral"
API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}"
headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}

# Check if model exists
try:
    print(f"Checking if model {MODEL_ID} exists...")
    response = requests.get(API_URL, headers=headers)
    print(f"Status: {response.status_code}")
    if response.status_code == 200:
        print("Model exists and is accessible")
        print(f"Response: {response.text[:200]}...")
    else:
        print(f"Response: {response.text}")
except Exception as e:
    print(f"Error checking model: {str(e)}")

# Global variable to track model status
model_loaded = False
estimated_time = None
use_simple_format = True  # Toggle to use simpler format instead of chat format

def format_prompt(messages):
    """Format chat messages into a text prompt that Mistral models can understand"""
    if use_simple_format:
        # Simple format - just extract the message content
        system = next((m["content"] for m in messages if m["role"] == "system"), "")
        last_user_msg = next((m["content"] for m in reversed(messages) if m["role"] == "user"), "")
        
        if system:
            return f"{system}\n\nQuestion: {last_user_msg}\n\nAnswer:"
        else:
            return f"Question: {last_user_msg}\n\nAnswer:"
    else:
        # Chat format for Mistral models
        formatted = ""
        for msg in messages:
            if msg["role"] == "system":
                formatted += f"<s>[INST] {msg['content']} [/INST]</s>\n"
            elif msg["role"] == "user":
                formatted += f"<s>[INST] {msg['content']} [/INST]"
            elif msg["role"] == "assistant":
                formatted += f" {msg['content']} </s>\n"
        return formatted

def query_model_text_generation(prompt, parameters=None):
    """Query the model using the text generation API endpoint"""
    payload = {
        "inputs": prompt,
    }
    
    if parameters:
        payload["parameters"] = parameters
    
    print(f"Sending text generation query to API...")
    print(f"Prompt: {prompt[:100]}...")
    
    try:
        # Try with longer timeout
        response = requests.post(
            API_URL, 
            headers=headers, 
            json=payload,
            timeout=180  # 3 minute timeout
        )
        
        print(f"API response status: {response.status_code}")
        
        # If successful, return the response
        if response.status_code == 200:
            print(f"Success! Response: {str(response.text)[:200]}...")
            return response.json()
            
        # If model is loading, handle it
        elif response.status_code == 503 and "estimated_time" in response.json():
            est_time = response.json()["estimated_time"]
            global estimated_time
            estimated_time = est_time
            print(f"Model is loading. Estimated time: {est_time:.2f} seconds")
            return None
            
        # For other errors
        else:
            print(f"API error: {response.text}")
            return None
            
    except Exception as e:
        print(f"Request exception: {str(e)}")
        return None

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    """Respond to user messages"""
    
    # Create the messages list
    messages = [{"role": "system", "content": system_message}]
    
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})
    
    messages.append({"role": "user", "content": message})
    
    # Format the prompt
    prompt = format_prompt(messages)
    
    # Set up the generation parameters
    parameters = {
        "max_new_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "do_sample": True,
        "return_full_text": False  # Only return the generated text, not the prompt
    }
    
    # Initial message about model status
    global estimated_time
    if estimated_time:
        initial_msg = f"⌛ The model is loading... estimated time: {estimated_time:.0f} seconds. Please be patient."
    else:
        initial_msg = "⌛ Working on your request..."
    
    yield initial_msg
    
    # Try multiple times with increasing waits
    max_retries = 6
    for attempt in range(max_retries):
        # Check if this is a retry
        if attempt > 0:
            wait_time = min(60, 10 * attempt)
            yield f"⌛ Still working on your request... (attempt {attempt+1}/{max_retries})"
            time.sleep(wait_time)
        
        try:
            # Query the model using text generation
            result = query_model_text_generation(prompt, parameters)
            
            if result:
                # Handle different response formats
                if isinstance(result, list) and len(result) > 0:
                    if isinstance(result[0], dict) and "generated_text" in result[0]:
                        yield result[0]["generated_text"]
                        return
                
                if isinstance(result, dict) and "generated_text" in result:
                    yield result["generated_text"]
                    return
                    
                # String or other format
                yield str(result)
                return
            
            # If model is still loading, get the latest estimate
            if estimated_time and attempt < max_retries - 1:
                try:
                    response = requests.get(API_URL, headers=headers)
                    if response.status_code == 503 and "estimated_time" in response.json():
                        estimated_time = response.json()["estimated_time"]
                        print(f"Updated loading time: {estimated_time:.0f} seconds")
                except:
                    pass
                
        except Exception as e:
            print(f"Error in attempt {attempt+1}: {str(e)}")
            if attempt == max_retries - 1:
                yield f"""❌ Sorry, I couldn't generate a response after multiple attempts.

Error details: {str(e)}

Please try again later or contact support if this persists."""

    # If all retries failed
    yield """❌ The model couldn't be accessed after multiple attempts.

This could be due to:
1. Heavy server load
2. The model being too large for the current hardware
3. Temporary service issues

Please try again later. For best results with large models like Mistral-7B, consider:
- Using a smaller model
- Creating a 4-bit quantized version
- Using Hugging Face Inference Endpoints instead of Spaces"""


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a data management expert specializing in Microsoft 365 services.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
    description="""This interface uses a fine-tuned Mistral model for Microsoft 365 data management.
    First requests may take 2-3 minutes as the model loads."""
)


if __name__ == "__main__":
    demo.launch()