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| import streamlit as st | |
| import openai | |
| # Function to interact with GPT-3 model | |
| def ask_gpt3_personalized(prompt, height_cm, weight_kg, age): | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": "You are a chatbot that will answer queries related to fitness and nutrition. The chatbot should understand questions about workout routines, dietary advice, and general fitness tips. Chatbot will offer personalized workout and diet plans based on user inputs such as body type, fitness goals, and dietary restrictions. this is the chat history use this as a reference to answer the queries"+st.session_state.messages}, | |
| {"role": "user", "content": "my height is " + str(height_cm) + " cm, my weight is " + str(weight_kg) + " kg, and I am " + str(age) + " years old."+prompt}, | |
| ] | |
| ) | |
| return response['choices'][0]['message']['content'] | |
| def ask_gpt3(prompt): | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": "You are a chatbot that will answer queries related to fitness and nutrition. The chatbot should understand questions about workout routines, dietary advice, and general fitness tips. Chatbot will offer personalized workout and diet plans based on user inputs such as body type, fitness goals, and dietary restrictions. this is the chat history use this as a reference to answer the queries"+st.session_state.messages}, | |
| {"role": "user", "content": prompt}, | |
| ] | |
| ) | |
| return response['choices'][0]['message']['content'] | |
| # Main function to run the Streamlit app | |
| def main(): | |
| st.title("Fitness Chatbot") | |
| # add a sibebar that can take in user input for the chatbot prompt and the API key | |
| st.sidebar.title("Personal Information") | |
| openai.api_key = st.sidebar.text_input("Your OpenAI API Key here", "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") | |
| height_cm = st.sidebar.number_input("Height (cm)", 0, 300) | |
| weight_kg = st.sidebar.number_input("Weight (kg)", 0, 300) | |
| age = st.sidebar.number_input("Age", 0, 100) | |
| # Initialize conversation history | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| # User input | |
| user_input = st.chat_input("Ask something") | |
| # If user input is not empty | |
| if user_input: | |
| # Add user input to conversation history | |
| st.session_state.messages.append({"role": "user", "content": user_input}) | |
| # Get chatbot response | |
| if height_cm and weight_kg and age: | |
| chatbot_response = ask_gpt3_personalized(user_input, height_cm, weight_kg, age) | |
| else: | |
| chatbot_response = ask_gpt3(user_input) | |
| # Add chatbot response to conversation history | |
| st.session_state.messages.append({"role": "assistant", "content": chatbot_response}) | |
| # Display conversation history | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # Clear the chat input | |
| st.session_state["chat_input"] = "" | |
| # Run the main function | |
| if __name__ == "__main__": | |
| main() |