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Update app.py

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# 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()

	# 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()