Feat add db nps

.replit

@@ -1,4 +1,4 @@
-modules = ["python-3.12"]
+modules = ["python-3.12", "postgresql-16"]
 run = "streamlit run --server.headless true main.py"
 
 [nix]

database.py

@@ -0,0 +1,49 @@
+
+import os
+import psycopg2
+from datetime import datetime
+
+def init_db():
+    """Initialize the database and create tables if needed"""
+    try:
+        conn = psycopg2.connect(os.environ['DATABASE_URL'])
+        cur = conn.cursor()
+
+        # Create feedback table
+        cur.execute('''
+            CREATE TABLE IF NOT EXISTS feedback (
+                id SERIAL PRIMARY KEY,
+                session_id TEXT,
+                search_term TEXT,
+                nps_score INTEGER,
+                email TEXT,
+                comments TEXT,
+                created_at TIMESTAMP
+            )
+        ''')
+
+        conn.commit()
+    except Exception as e:
+        print(f"Database initialization error: {e}")
+    finally:
+        cur.close()
+        conn.close()
+
+def save_feedback(session_id, search_term, nps_score=None, email=None, comments=None):
+    """Save feedback to PostgreSQL database"""
+    try:
+        conn = psycopg2.connect(os.environ['DATABASE_URL'])
+        cur = conn.cursor()
+
+        cur.execute(
+            '''
+            INSERT INTO feedback (session_id, search_term, nps_score, email, comments, created_at)
+            VALUES (%s, %s, %s, %s, %s, %s)
+            ''',
+            (session_id, search_term, nps_score, email, comments, datetime.now())
+        )
+
+        conn.commit()
+    finally:
+        cur.close()
+        conn.close()

main.py

@@ -1,4 +1,3 @@
-
 """
 Situate Learning - A Streamlit app that generates higher-order thinking questions
 using Groq's LLM API based on teacher input.
@@ -47,9 +46,13 @@ def generate_questions(lesson_text):
         str: Generated questions from the LLM
     """
     try:
-        system_msg = "You are an enthusiastic, curious teacher assistant creating thought-provoking questions."
-        user_msg = (f"Teacher: {lesson_text} Can you create some engaging, "
-                   "higher-order thinking questions related to this topic? Include interdisciplinary questions.")
+        system_msg = ("You are an enthusiastic, curious teacher assistant "
+                     "creating thought-provoking questions.")
+        user_msg = (
+            f"Teacher: {lesson_text} Can you create some engaging, "
+            "higher-order thinking questions related to this topic? "
+            "Include interdisciplinary questions."
+        )
         messages = [
             {"role": "system", "content": system_msg},
             {"role": "user", "content": user_msg}
@@ -79,9 +82,7 @@ def copy_to_clipboard_script(response):
 def main():
     """Main function to run the Streamlit app."""
     initialize_session_state()
-
     st.title("🌟 Situate Learning")
-
     st.markdown("### What did you teach today?")
     st.session_state.teacher_input = st.text_input(
         "Enter today's lesson or topic:",
@@ -91,6 +92,10 @@ def main():
 
     if st.button("Generate Questions"):
         if st.session_state.teacher_input.strip():
+            # Log the search term immediately
+            from database import save_feedback
+            save_feedback(st.session_state.session_uuid, st.session_state.teacher_input)
+
             st.session_state.ai_response = generate_questions(st.session_state.teacher_input)
             st.markdown("### Higher-Order Thinking Questions:")
             st.write(st.session_state.ai_response)
@@ -101,20 +106,28 @@ def main():
         st_copy_to_clipboard(st.session_state.ai_response)
         st.markdown("---")
 
-        st.markdown(
-            """
-            <div style='text-align: center;'>
-                <a href="https://leekahhow.notion.site/14ac34bc89df803fbb5fc9b2922a62ea?pvs=105" 
-                target="_blank">Provide Feedback</a>
-            </div>
-            """,
-            unsafe_allow_html=True
-        )
+        from streamlit_star_rating import st_star_rating
+        st.markdown("### How helpful were these questions?")
+        stars = st_star_rating("", maxValue=5, defaultValue=5, key="rating")
+
+        if stars:
+            email = st.text_input("Email (optional)")
+            feedback = st.text_area("Additional comments (optional)")
+            if st.button("Submit"):
+                from database import save_feedback
+                save_feedback(st.session_state.session_uuid, 
+                            st.session_state.teacher_input,
+                            stars,
+                            email,
+                            feedback)
+                st.success("Thanks for your feedback! 🌟")
 
     st.markdown(
         f"<div style='text-align: center; color: grey;'>Session ID: {st.session_state.session_uuid}</div>",
         unsafe_allow_html=True
     )
 
 if __name__ == "__main__":
-    main()
+    from database import init_db
+    init_db()
+    main()

requirements.txt

@@ -1,4 +1,9 @@
 streamlit
 groq
 st-copy-to-clipboard
-beautifulsoup4
+beautifulsoup4
+replit
+replit
+st-star-rating
+psycopg2-binary
+psycopg2-binary

test_main.py

@@ -22,9 +22,15 @@ def test_generate_questions(self, mock_groq):
 
         # Verify Groq was called with correct parameters
         expected_msg = [
-            {"role": "system", "content": "You are an enthusiastic, curious teacher assistant creating thought-provoking questions."},
-            {"role": "user", "content": "Teacher: test lesson Can you create some engaging, "
-             "higher-order thinking questions related to this topic? Include interdisciplinary questions."}
+            {
+                "role": "system",
+                "content": "You are an enthusiastic, curious teacher assistant creating thought-provoking questions."
+            },
+            {
+                "role": "user",
+                "content": "Teacher: test lesson Can you create some engaging, "
+                          "higher-order thinking questions related to this topic? Include interdisciplinary questions."
+            }
         ]
         mock_groq.return_value.chat.completions.create.assert_called_with(
             model="llama-3.1-8b-instant",