Create app.py

app.py

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+import streamlit as st
+import openai
+
+# Initialize the OpenAI API
+openai.api_key = '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'
+
+KNOWN_MODELS = [
+    "Neural Networks", "Decision Trees", "Support Vector Machines", 
+    "Random Forests", "Linear Regression", "Reinforcement Learning"
+]
+
+def recommend_ai_model_via_gpt(description):
+    # ... [same as before]
+
+def explain_recommendation(model_name):
+    # ... [same as before]
+
+# 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.")
+