"""
Minimal Gradio interface for a simple AI assistant without smolagents

This is a standalone version that uses only Hugging Face Inference API directly.
It creates a simple Gradio interface for text generation.
"""

import os
import sys
import json
import requests
import gradio as gr

# Check if running in Hugging Face Spaces
IS_HF_SPACES = os.environ.get("SPACE_ID") is not None

class MinimalAIAssistant:
    """
    Minimal AI Assistant using Hugging Face Inference API directly
    """
    def __init__(self, api_key=None, model_id="mistralai/Mixtral-8x7B-Instruct-v0.1"):
        """
        Initialize the minimal AI assistant
        
        Args:
            api_key: Hugging Face API key
            model_id: Model ID to use
        """
        self.api_key = api_key or os.environ.get("HF_API_KEY", "")
        self.model_id = model_id
        self.api_url = f"https://api-inference.huggingface.co/models/{model_id}"
        self.headers = {"Authorization": f"Bearer {self.api_key}"}
        
        # System prompt
        self.system_prompt = """
You are an advanced AI assistant designed to help with various tasks.
You can answer questions, provide information, and assist with problem-solving.
Always be helpful, accurate, and concise in your responses.
"""

    def query(self, prompt):
        """
        Query the model with a prompt
        
        Args:
            prompt: User prompt
            
        Returns:
            Model response
        """
        try:
            # Format the prompt with system message
            formatted_prompt = f"{self.system_prompt}\n\nUser: {prompt}\n\nAssistant:"
            
            # Prepare the payload
            payload = {
                "inputs": formatted_prompt,
                "parameters": {
                    "max_new_tokens": 1024,
                    "temperature": 0.7,
                    "top_p": 0.95,
                    "do_sample": True
                }
            }
            
            # Make the API request
            response = requests.post(self.api_url, headers=self.headers, json=payload)
            
            # Check for errors
            if response.status_code != 200:
                return f"Error: API returned status code {response.status_code}. {response.text}"
            
            # Parse the response
            result = response.json()
            
            # Extract the generated text
            if isinstance(result, list) and len(result) > 0:
                generated_text = result[0].get("generated_text", "")
                # Remove the prompt from the response
                if generated_text.startswith(formatted_prompt):
                    generated_text = generated_text[len(formatted_prompt):].strip()
                return generated_text
            else:
                return "Error: Unexpected response format from API"
        
        except Exception as e:
            return f"Error querying model: {str(e)}"


def create_gradio_interface():
    """
    Create a Gradio interface for the minimal AI assistant
    
    Returns:
        Gradio interface
    """
    # Initialize the assistant
    assistant = MinimalAIAssistant()
    
    def process_query(query, api_key=""):
        """
        Process a user query
        
        Args:
            query: User query
            api_key: Hugging Face API key (optional)
            
        Returns:
            Assistant's response
        """
        # Update API key if provided
        if api_key:
            assistant.api_key = api_key
            assistant.headers = {"Authorization": f"Bearer {api_key}"}
        
        # Check if API key is set
        if not assistant.api_key:
            return "Error: No API key provided. Please enter your Hugging Face API key."
        
        # Process the query
        return assistant.query(query)
    
    # Create the interface
    with gr.Blocks(title="Minimal AI Assistant") as interface:
        gr.Markdown("# Minimal AI Assistant")
        gr.Markdown("""
        This is a minimal AI assistant using the Hugging Face Inference API.
        Enter your query below and the assistant will respond.
        """)
        
        api_key_input = gr.Textbox(
            label="Hugging Face API Key",
            placeholder="Enter your Hugging Face API key here...",
            type="password"
        )
        
        query_input = gr.Textbox(
            label="Your Query",
            placeholder="Enter your query here...",
            lines=3
        )
        
        submit_button = gr.Button("Submit")
        
        response_output = gr.Textbox(
            label="Assistant Response",
            lines=15
        )
        
        # Sample queries
        gr.Markdown("### Sample Queries")
        sample_queries = [
            "What is the capital of France?",
            "Explain the concept of machine learning in simple terms.",
            "Write a short poem about artificial intelligence."
        ]
        
        for query in sample_queries:
            sample_button = gr.Button(f"Try: {query}")
            sample_button.click(
                fn=lambda q=query: q,
                outputs=query_input
            )
        
        # Set up event handlers
        submit_button.click(
            fn=process_query,
            inputs=[query_input, api_key_input],
            outputs=response_output
        )
        
        # Add examples
        gr.Examples(
            examples=sample_queries,
            inputs=query_input
        )
    
    return interface


# Create and launch the interface
interface = create_gradio_interface()

# For Hugging Face Spaces
if __name__ == "__main__":
    interface.launch()