app.py

import gradio as gr
from huggingface_hub import InferenceClient

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def create_system_prompt(agent_type, personality, expertise_level, language):
    base_prompt = f"""You are a {agent_type} movie recommendation agent with the following characteristics:
- Personality: {personality}
- Expertise Level: {expertise_level}
- Language: {language}

Your role is to:
1. Understand user preferences and mood
2. Provide personalized movie recommendations
3. Explain why you're recommending specific movies
4. Maintain a {personality} tone throughout the conversation
5. Consider the user's expertise level ({expertise_level}) when explaining

Please respond in {language}."""
    return base_prompt

def respond(
    message,
    history: list[tuple[str, str]],
    agent_type,
    personality,
    expertise_level,
    language,
    max_tokens,
    temperature,
    top_p,
    genre,
    mood,
):
    # Create system prompt
    system_message = create_system_prompt(agent_type, personality, expertise_level, language)
    messages = [{"role": "system", "content": system_message}]

    # Add genre and mood information to user input
    enhanced_message = f"Genre: {genre}\nMood: {mood}\nUser request: {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": enhanced_message})

    response = ""
    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content
        response += token
        yield response

def reset_chat():
    return None

def show_settings_changed_info(agent_type, personality, expertise_level, language):
    return f"""
    New Agent Settings:
    - Type: {agent_type}
    - Personality: {personality}
    - Expertise Level: {expertise_level}
    - Response Language: {language}
    
    Chat has been reset. Please start a new conversation with the updated settings.
    """

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""

with gr.Blocks() as demo:
    gr.Markdown("""
    # 🎬 Personalized Movie Recommender
    
    Welcome to your personalized movie recommendation system!
    Tell us your preferred genres and current mood, and we'll recommend the perfect movies for you.
    """)
    
    with gr.Row():
        with gr.Column(scale=2):
            chatbot = gr.Chatbot(
                height=600,
                show_copy_button=True,
                avatar_images=("👤", "🎬"),
                bubble_full_width=False
            )
            with gr.Row():
                msg = gr.Textbox(
                    placeholder="What kind of movie are you looking for?",
                    show_label=False,
                    container=False
                )
                with gr.Row():
                    submit = gr.Button("Get Recommendations", variant="primary", size="sm")
                    clear = gr.Button("Clear Chat", size="sm")
        
        with gr.Column(scale=1):
            with gr.Group():
                gr.Markdown("### 🎯 Recommendation Settings")
                genre = gr.Dropdown(
                    choices=["Action", "Comedy", "Drama", "Romance", "Thriller", "Sci-Fi", "Fantasy", "Animation"],
                    label="Preferred Genres",
                    multiselect=True
                )
                mood = gr.Dropdown(
                    choices=["Exciting", "Emotional", "Suspenseful", "Relaxing", "Mysterious"],
                    label="Current Mood",
                    multiselect=True
                )
            
            with gr.Group():
                gr.Markdown("### 🤖 Agent Settings")
                agent_type = gr.Dropdown(
                    choices=["Expert", "Friend", "Film Critic", "Curator"],
                    label="Agent Type",
                    value="Expert"
                )
                personality = gr.Dropdown(
                    choices=["Friendly", "Professional", "Humorous", "Emotional", "Objective"],
                    label="Personality",
                    value="Friendly"
                )
                expertise_level = gr.Dropdown(
                    choices=["Beginner", "Intermediate", "Expert"],
                    label="Explanation Level",
                    value="Intermediate"
                )
                language = gr.Dropdown(
                    choices=["English", "Korean", "Japanese"],
                    label="Response Language",
                    value="English"
                )
            
            with gr.Group():
                gr.Markdown("### ⚙️ Advanced Settings")
                max_tokens = gr.Slider(
                    minimum=1,
                    maximum=2048,
                    value=512,
                    step=1,
                    label="Max Tokens"
                )
                temperature = gr.Slider(
                    minimum=0.1,
                    maximum=4.0,
                    value=0.7,
                    step=0.1,
                    label="Temperature"
                )
                top_p = gr.Slider(
                    minimum=0.1,
                    maximum=1.0,
                    value=0.95,
                    step=0.05,
                    label="Top-p"
                )

    # Reset chat and show notification when settings change
    for component in [agent_type, personality, expertise_level, language]:
        component.change(
            fn=show_settings_changed_info,
            inputs=[agent_type, personality, expertise_level, language],
            outputs=gr.Info()
        ).then(
            fn=reset_chat,
            outputs=chatbot
        )

    submit.click(
        respond,
        inputs=[
            msg,
            chatbot,
            agent_type,
            personality,
            expertise_level,
            language,
            max_tokens,
            temperature,
            top_p,
            genre,
            mood,
        ],
        outputs=chatbot,
    ).then(
        lambda: "",
        None,
        msg,
        queue=False
    )
    
    clear.click(lambda: None, None, chatbot, queue=False)


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