Update app.py

app.py

@@ -1,60 +1,94 @@
 import pandas as pd
 import streamlit as st
+import sqlite3
 
-# Title
-st.title("Q&A Evaluation Tool")
+# Database setup
+DB_FILE = "feedback.db"
 
-# Load the dataset from the repository
+def init_db():
+    # Connect to SQLite database
+    conn = sqlite3.connect(DB_FILE)
+    cursor = conn.cursor()
+
+    # Create a table for storing feedback if not exists
+    cursor.execute("""
+        CREATE TABLE IF NOT EXISTS feedback (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            question TEXT,
+            selected_answer TEXT,
+            rating INTEGER,
+            feedback_text TEXT
+        )
+    """)
+    conn.commit()
+    conn.close()
+
+def store_feedback(question, selected_answer, rating, feedback_text):
+    conn = sqlite3.connect(DB_FILE)
+    cursor = conn.cursor()
+    cursor.execute("""
+        INSERT INTO feedback (question, selected_answer, rating, feedback_text)
+        VALUES (?, ?, ?, ?)
+    """, (question, selected_answer, rating, feedback_text))
+    conn.commit()
+    conn.close()
+
+# Initialize database
+init_db()
+
+# Load Q&A data
 @st.cache_data
 def load_data():
     return pd.read_csv("dummy_qa_data.csv")
 
-# Load data
 data = load_data()
 
-# Add columns for evaluation if not present
-if "Rating" not in data.columns:
-    data["Rating"] = ""
-if "Comments" not in data.columns:
-    data["Comments"] = ""
-
-# Initialize session state for tracking progress
+# Session state for question navigation
 if "current_index" not in st.session_state:
     st.session_state.current_index = 0
 
-# Display current question-answer pair
-idx = st.session_state.current_index
-if idx < len(data):
-    st.subheader(f"Question {idx + 1}:")
-    st.write(data.loc[idx, "Question"])
+# Current question index
+current_index = st.session_state.current_index
 
-    st.subheader("Generated Answer:")
-    st.write(data.loc[idx, "Generated Answer"])
+# Display question and options
+if 0 <= current_index < len(data):
+    question = data.loc[current_index, "Question"]
+    answers = eval(data.loc[current_index, "Generated Answer"])  # Convert string to list of tuples
 
-    # Rating input
-    rating = st.slider("Rate the answer (1 = Poor, 5 = Excellent)", 1, 5, step=1)
-    comment = st.text_area("Add any comments or suggestions")
+    st.subheader(f"Question {current_index + 1}: {question}")
+    selected_answer = st.radio("Choose the best answer:", options=[ans[0] for ans in answers])
 
-    # Save feedback
-    if st.button("Submit Feedback"):
-        data.at[idx, "Rating"] = rating
-        data.at[idx, "Comments"] = comment
-        st.session_state.current_index += 1
+    # Rating
+    rating = st.slider("Rate the answer (1 = Worst, 5 = Best)", 1, 5, value=3)
 
-        # Save the updated dataset
-        data.to_csv("evaluated_data.csv", index=False)
+    # Free-text feedback
+    feedback_text = st.text_area("Any additional feedback?")
 
-        # Confirmation and next question
-        st.success("Feedback submitted!")
-        st.experimental_rerun()
+    # Navigation buttons
+    col1, col2, col3 = st.columns([1, 1, 2])
+
+    with col1:
+        if st.button("Back") and current_index > 0:
+            st.session_state.current_index -= 1
+            st.experimental_rerun()
+
+    with col2:
+        if st.button("Next"):
+            # Store feedback for the current question
+            store_feedback(question, selected_answer, rating, feedback_text)
+
+            if current_index < len(data) - 1:
+                st.session_state.current_index += 1
+                st.experimental_rerun()
+            else:
+                st.success("You have completed all questions!")
+                st.stop()
 else:
-    st.write("You have completed the evaluation. Thank you!")
-
-    # Download the evaluated file
-    with open("evaluated_data.csv", "rb") as f:
-        st.download_button(
-            "Download Evaluated Data",
-            f,
-            file_name="evaluated_data.csv",
-            mime="text/csv",
-        )
+    st.write("No more questions available!")
+
+# View results for debugging (optional)
+if st.checkbox("Show Feedback Database (Admin Use)"):
+    conn = sqlite3.connect(DB_FILE)
+    df = pd.read_sql_query("SELECT * FROM feedback", conn)
+    st.dataframe(df)
+    conn.close()