app.py

import os
import openai
import gradio as gr

# Retrieve credentials from environment variables
openai_api_key = os.getenv("OPENAI_API_KEY","xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")
openai_model = "gpt-3.5-turbo"

# Initialize OpenAI client
client = openai.OpenAI(api_key=openai_api_key)

# Define the Fitness Assistant system message
system_message = """You are a Smart Fitness Assistant designed to help users with their fitness-related inquiries.
You can assist with workout plans, exercise routines, nutrition guidance, calorie tracking, personalized fitness recommendations, recovery tips, and health monitoring insights.
Please provide relevant details like fitness goals, experience level, and any health considerations for more precise responses.
Note: I do not provide medical diagnoses or professional healthcare advice—always consult a certified professional for medical concerns.
"""

# Store conversation history
messages_array = [{"role": "system", "content": system_message}]

def fitness_response(user_query, history):
    global messages_array

    # Append user input to messages array
    messages_array.append({"role": "user", "content": user_query})

    # Call OpenAI API
    response = client.chat.completions.create(
        model=openai_model,
        temperature=0.7,
        max_tokens=1200,
        messages=messages_array
    )

    # Extract assistant response
    generated_text = response.choices[0].message.content

    # Append assistant response to messages array
    messages_array.append({"role": "assistant", "content": generated_text})

    # Return generated text
    return generated_text

# Create Gradio interface
chatbot = gr.ChatInterface(
    fn=fitness_response,
    title="Fitness APP",
    type="messages",  # ✅ Use OpenAI-style message format
)

# Launch the chatbot
if __name__ == "__main__":
    chatbot.launch()