Update app.py

app.py

@@ -2,7 +2,7 @@ import streamlit as st
 import openai
 
 # Initialize the OpenAI API
-openai.api_key = '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'
+openai.api_key = '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'  # Remember never to expose API keys in code
 
 KNOWN_MODELS = [
     "Neural Networks", "Decision Trees", "Support Vector Machines", 
@@ -10,48 +10,34 @@ KNOWN_MODELS = [
 ]
 
 def recommend_ai_model_via_gpt(description):
-    # Formulate a prompt for the large language model
-    prompt = f"Given the application described as: '{description}', which AI model would be most suitable?"
+    messages = [
+        {"role": "user", "content": f"Given the application described as: '{description}', which AI model would be most suitable?"}
+    ]
 
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        prompt=prompt,
-        max_tokens=50
-    )
-    
-    recommendation = response.choices[0].text.strip()
-    return recommendation
+    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):
-    # Formulate a prompt for explanation
-    prompt = f"Why would {model_name} be a suitable choice for the application?"
-
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        prompt=prompt,
-        max_tokens=150
-    )
+    messages = [
+        {"role": "user", "content": f"Why would {model_name} be a suitable choice for the application?"}
+    ]
     
-    explanation = response.choices[0].text.strip()
-    return explanation
-
-def get_feedback():
-    feedback = input("Was this recommendation helpful? (yes/no): ").lower()
-    if feedback == 'yes':
-        print("Thank you for your feedback!")
-    else:
-        print("Thank you! We'll strive to improve.")
-
-def rate_explanation():
     try:
-        rating = int(input("Rate the explanation from 1 (worst) to 5 (best): "))
-        if 1 <= rating <= 5:
-            print("Thank you for rating!")
-        else:
-            print("Invalid rating. Please rate between 1 and 5.")
-    except ValueError:
-        print("Invalid input. Please enter a number between 1 and 5.")
-
+        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')
@@ -77,4 +63,3 @@ if st.button("Recommend AI Model"):
                 st.success("Thank you for your feedback!")
     else:
         st.warning("Please provide a description.")
-