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import streamlit as st
import openai
# Initialize the OpenAI API
openai.api_key = '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' # Remember never to expose API keys in code
KNOWN_MODELS = [
"Neural Networks", "Decision Trees", "Support Vector Machines",
"Random Forests", "Linear Regression", "Reinforcement Learning"
]
def recommend_ai_model_via_gpt(description):
messages = [
{"role": "user", "content": f"Given the application described as: '{description}', which AI model would be most suitable?"}
]
try:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=messages
)
recommendation = response['choices'][0]['message']['content'].strip()
return recommendation
except openai.error.OpenAIError as e:
return f"Error: {e}"
def explain_recommendation(model_name):
messages = [
{"role": "user", "content": f"Why would {model_name} be a suitable choice for the application?"}
]
try:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=messages
)
explanation = response['choices'][0]['message']['content'].strip()
return explanation
except openai.error.OpenAIError as e:
return f"Error: {e}"
# Streamlit UI
st.title('AI Model Recommender')
description = st.text_area("Describe your application:", "")
if st.button("Recommend AI Model"):
if description:
recommended_model = recommend_ai_model_via_gpt(description)
# Validate recommended model
if recommended_model not in KNOWN_MODELS:
st.warning("The recommendation is ambiguous. Please refine your description or consult an expert.")
else:
st.subheader(f"Recommended AI Model: {recommended_model}")
explanation = explain_recommendation(recommended_model)
st.write("Reason:", explanation)
# Collecting rating and feedback through Streamlit
rating = st.slider("Rate the explanation from 1 (worst) to 5 (best):", 1, 5)
feedback = st.text_input("Any additional feedback?")
if st.button("Submit Feedback"):
st.success("Thank you for your feedback!")
else:
st.warning("Please provide a description.")
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