app.py

# Fitora: AI Fitness Chatbot
# Run with: pip install gradio huggingface_hub

import gradio as gr
from huggingface_hub import InferenceClient

# Create Hugging Face inference client
# Make sure you have a valid Hugging Face token if required
client = InferenceClient("microsoft/phi-4")

# Chatbot logic
def respond(message, history):
    # System prompt for fitness personality
    messages = [
        {
            "role": "system",
            "content": (
                "You are Fitora, an upbeat, energetic personal fitness trainer AI. "
                "You give motivational, supportive responses with specific workout tips, "
                "nutrition advice, and healthy lifestyle encouragement. Always be positive "
                "and help the user stay consistent in their fitness journey."
            )
        }
    ]
    
    # Add previous chat history if it exists
    if history:
        messages.extend(history)

    # Add user message
    messages.append({"role": "user", "content": message})
    
    # Get AI response from Hugging Face
    response = client.chat_completion(
        messages,
        max_tokens=500
    )
    
    return response['choices'][0]['message']['content'].strip()

# Build Gradio interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 🏋️‍♀️ **Fitora — Your AI Fitness Coach**  
        Your virtual workout buddy!  
        Get workout plans, nutrition tips, and a boost of motivation.  
        Let’s reach your goals together 💪🔥
        """
    )
    chatbot = gr.ChatInterface(
        respond,
        type="messages",
        title="Fitora - AI Fitness Coach",
        examples=[
            ["Give me a 15-minute HIIT workout for home"],
            ["Motivate me to do a workout after a long day"],
            ["Suggest a quick healthy breakfast"],
            ["What’s a good stretching routine before running?"]
        ]
    )

# Launch app
if __name__ == "__main__":
    demo.launch()