import gradio as gr
from huggingface_hub import InferenceClient
import os

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=os.getenv("HF_TOKEN"))

# 💡 Dynamic prompt builder based on CEFR level
def level_to_prompt(level):
    return {
        "A1": "You are a friendly French tutor. Speak mostly in English, use simple French, and explain everything.",
        "A2": "You are a patient French tutor. Use short French phrases and explain them in English.",
        "B1": "You are a helpful French tutor. Speak mostly in French but clarify in English when needed.",
        "B2": "You are a French tutor. Speak primarily in French with rare English support.",
        "C1": "You are a native French tutor. Speak entirely in French, clearly and professionally.",
        "C2": "You are a native French professor. Speak in rich, complex French. Avoid English."
    }.get(level, "You are a helpful French tutor.")

# Custom background CSS
css = """
@import url('https://fonts.googleapis.com/css2?family=Noto+Sans+JP&family=Playfair+Display&display=swap');
body {
  background-image: url('https://cdn-uploads.huggingface.co/production/uploads/67351c643fe51cb1aa28f2e5/wuyd5UYTh9jPrMJGmV9yC.jpeg');
  background-size: cover;
  background-position: center;
  background-repeat: no-repeat;
}
.gradio-container {
  display: flex;
  flex-direction: column;
  justify-content: center;
  min-height: 100vh;
  padding-top: 2rem;
  padding-bottom: 2rem;
}  
#chat-panel {
  background-color: rgba(255, 255, 255, 0.85);
  padding: 2rem;
  border-radius: 12px;
  max-width: 700px;
  height: 70vh;
  margin: auto;
  box-shadow: 0 0 12px rgba(0, 0, 0, 0.3);
  overflow-y: auto;
}
.gradio-container .chatbot h1 {
  color: var(--custom-title-color) !important;
  font-family: 'Playfair Display', serif !important;
  font-size: 5rem !important;
  font-weight: bold !important;
  text-align: center !important;
  margin-bottom: 1.5rem !important;
  width: 100%;
}
"""

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Chat logic
def respond(message, history, level, max_tokens, temperature, top_p):
    system_message = level_to_prompt(level)
    messages = [{"role": "system", "content": system_message}]
    
    # Handle history based on its format
    if history and isinstance(history[0], dict):
        # New format (messages with role/content)
        messages.extend(history)
    else:
        # Old format (tuples)
        for user, bot in history:
            if user:
                messages.append({"role": "user", "content": user})
            if bot:
                messages.append({"role": "assistant", "content": bot})
    
    # Add current message
    messages.append({"role": "user", "content": message})
    
    # Generate response
    response = ""
    try:
        for msg in client.chat_completion(
            messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p
        ):
            token = msg.choices[0].delta.content
            if token is not None:  # Handle None tokens
                response += token
            yield response
    except Exception as e:
        print(f"Error in chat completion: {e}")
        yield f"Désolé! There was an error: {str(e)}"

# UI layout
with gr.Blocks(css=css) as demo:
    gr.Markdown("French Tutor", elem_id="custom-title")
    with gr.Column(elem_id="chat-panel"):
        with gr.Accordion("⚙️ Advanced Settings", open=False):
            level = gr.Dropdown(
                choices=["A1", "A2", "B1", "B2", "C1", "C2"],
                value="A1",
                label="Your French Level (CEFR)"
            )
            max_tokens = gr.Slider(1, 2048, value=512, step=1, label="Response Length")
            temperature = gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Creativity")
            top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Dynamic Text")
        
        gr.ChatInterface(
            fn=respond,
            additional_inputs=[level, max_tokens, temperature, top_p],
            type="messages"  # ✅ prevents deprecation warning
        )

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