import gradio as gr
import os
from huggingface_hub import InferenceClient

# Initialize the Hugging Face Inference Client with token from environment
# Get token from HuggingFace Spaces secrets or environment variable
hf_token = os.environ.get("HF_TOKEN", None)
client = InferenceClient(token=hf_token)

def chatbot(question, model, temperature):
    """Send the question to HuggingFace model using the Inference API.
    
    This function sends your question to a HuggingFace model and returns the response.
    """
    if not question:
        return "Please enter a question."
    
    try:
        # Call HuggingFace Inference API with the selected model
        response = client.chat_completion(
            model=model,
            messages=[{"role": "user", "content": question}],
            temperature=float(temperature),
            max_tokens=500
        )
        
        # Extract the response content
        content = response.choices[0].message.content
        
        # Include model and temperature information in the returned text
        return f"Model: {model}\nTemperature: {temperature}\n\n{content}"
    
    except Exception as e:
        error_msg = str(e)
        if "api_key" in error_msg.lower() or "authentication" in error_msg.lower():
            return """⚠️ Authentication Error: This model requires a HuggingFace token.

Please follow these steps:

1. Get a FREE token from: https://huggingface.co/settings/tokens
   - Click "New token"
   - Name it (e.g., "chatbot-token")
   - Select "Read" permission
   - Click "Create token" and copy it

2. Add the token to your Space:
   - Go to your Space Settings → "Variables and secrets"
   - Click "New secret"
   - Name: HF_TOKEN
   - Value: [paste your token]
   - Save

3. Restart your Space (it will rebuild automatically)

OR try selecting a different model that doesn't require authentication."""
        else:
            return f"Error: {error_msg}\n\nPlease try again or select a different model."


def main():
    # Build a simple Gradio app with a left-side control panel (model + temperature)
    with gr.Blocks(title="HuggingFace Chatbot") as demo:
        gr.Markdown("# 🤗 HuggingFace Chatbot")
        gr.Markdown("Ask questions and get responses from various HuggingFace AI models!")
        
        # Show token status
        if hf_token:
            gr.Markdown("✅ **Status**: HuggingFace token configured")
        else:
            gr.Markdown("⚠️ **Status**: No HuggingFace token found. Some models may not work. See instructions below if you get errors.")
        
        with gr.Row():
            # Left column: grouped controls (model dropdown + temperature slider)
            with gr.Column(scale=1):
                gr.Markdown("**Model & Settings**")
                model_dropdown = gr.Dropdown(
                    label="Model",
                    choices=[
                        "meta-llama/Llama-3.2-1B-Instruct",
                        "meta-llama/Llama-3.2-3B-Instruct",
                        "microsoft/Phi-3-mini-4k-instruct",
                        "HuggingFaceH4/zephyr-7b-beta",
                        "mistralai/Mistral-7B-Instruct-v0.3",
                        "google/gemma-2-2b-it"
                    ],
                    value="meta-llama/Llama-3.2-1B-Instruct",
                )
                temp_slider = gr.Slider(
                    label="Temperature",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.01,
                    value=0.7,
                    info="Higher values make output more random"
                )

            # Right column: input and output
            with gr.Column(scale=3):
                question = gr.Textbox(
                    label="Question", 
                    lines=3, 
                    placeholder="Type your message here..."
                )
                submit = gr.Button("Submit", variant="primary")
                output = gr.Textbox(label="Response", lines=15)

        # Wire up the button to call the chatbot function with model and temperature
        submit.click(
            fn=chatbot, 
            inputs=[question, model_dropdown, temp_slider], 
            outputs=output
        )
        
        # Also allow pressing Enter to submit
        question.submit(
            fn=chatbot,
            inputs=[question, model_dropdown, temp_slider],
            outputs=output
        )

    demo.launch()


if __name__ == "__main__":
    main()