update

pages/2_batch_evaluation.py

@@ -0,0 +1,109 @@
+import pandas as pd
+import streamlit as st
+from util.evaluator import evaluator, write_evaluation_commentary
+import os
+
+# Predefined examples
+examples = {
+    'good': {
+        'question': "What causes rainbows to appear in the sky?",
+        'explanation': "Rainbows appear when sunlight is refracted, dispersed, and reflected inside water droplets in the atmosphere, resulting in a spectrum of light appearing in the sky."
+    },
+    'bad': {
+        'question': "What causes rainbows to appear in the sky?",
+        'explanation': "Rainbows happen because light in the sky gets mixed up and sometimes shows colors when it's raining or when there is water around."
+    }
+}
+
+# Function to check password
+def check_password():
+    with st.sidebar:
+        password_input = st.text_input("Enter Password:", type="password")
+        submit_button = st.button("Submit")
+        if submit_button:
+            if password_input == os.getenv('PASSWORD'):
+                st.session_state['password_verified'] = True
+                st.experimental_rerun()
+            else:
+                st.error("Incorrect Password, please try again.")
+
+# Function to evaluate batch data
+def evaluate_batch(uploaded_file):
+    df = pd.read_csv(uploaded_file)
+    eval_instance = evaluator(model_name=st.session_state['model_name'])
+    results = []
+
+    for _, row in df.iterrows():
+        scores = eval_instance(row['question'], row['explanation'])
+        commentary = write_evaluation_commentary(scores)
+        result = {
+            'Question': row['question'],
+            'Explanation': row['explanation'],
+            **{c['Principle']: c['Score'] for c in commentary}
+        }
+        results.append(result)
+
+    return pd.DataFrame(results)
+
+# Main app logic
+def main():
+    st.title('Natural Language Explanation Demo')
+
+    model_name = st.selectbox('Select a model:', ['gpt4-1106', 'gpt35-1106'])
+    st.session_state['model_name'] = model_name  # Save model name to session state for use in batch processing
+
+    input_type = st.radio("Choose input type:", ('Use predefined example', 'Enter your own', 'Upload CSV for batch evaluation'))
+
+    if input_type == 'Use predefined example':
+        example_type = st.radio("Select an example type:", ('good', 'bad'))
+        question = examples[example_type]['question']
+        explanation = examples[example_type]['explanation']
+    elif input_type == 'Enter your own':
+        question = st.text_input('Enter your question:', '')
+        explanation = st.text_input('Enter your explanation:', '')
+    else:
+        uploaded_file = st.file_uploader("Upload a CSV file", type='csv')
+        if uploaded_file and st.button('Evaluate Batch'):
+            result_df = evaluate_batch(uploaded_file)
+            st.write('### Evaluated Results')
+            st.dataframe(result_df)
+            csv = result_df.to_csv(index=False)
+            st.download_button(
+                label="Download evaluated results as CSV",
+                data=csv,
+                file_name='batch_evaluation_results.csv',
+                mime='text/csv'
+            )
+            return
+
+    if st.button('Evaluate Explanation'):
+        if question and explanation:
+            eval_instance = evaluator(model_name)
+            scores = eval_instance(question, explanation)
+            st.write('### Scores')
+            details = write_evaluation_commentary(scores)
+            df = pd.DataFrame(details)
+            st.write(df)
+            data = {
+                'Question': question,
+                'Explanation': explanation,
+                **{detail['Principle']: detail['Score'] for detail in details}
+            }
+            df = pd.DataFrame([data])
+
+            # Convert DataFrame to CSV for download
+            csv = df.to_csv(index=False)
+            st.download_button(
+                label="Download evaluation as CSV",
+                data=csv,
+                file_name='evaluation.csv',
+                mime='text/csv',
+            )
+        else:
+            st.error('Please enter both a question and an explanation to evaluate.')
+
+if __name__ == '__main__':
+    if 'password_verified' not in st.session_state or not st.session_state['password_verified']:
+        check_password()
+    else:
+        main()