update

pages/3_explanation_generation.py

@@ -0,0 +1,134 @@
+import pandas as pd
+import streamlit as st
+from util.assistants import GPTAgent
+import os
+
+
+# Function to generate explanations based on a template
+def generate_explanations(model_name, questions, template, custom_template=None):
+    agent = GPTAgent(model_name)
+    explanations = []
+    progress_bar = st.progress(0)
+    total_questions = len(questions)
+
+    for i, question in enumerate(questions):
+        if template == "Chain of Thought":
+            prompt = f"""Generate an explanation using the Chain of Thought template for the following question:
+
+            Question: {question}
+
+            Let's think step by step.
+
+            Explanation:
+            """
+        elif template == "Custom" and custom_template:
+            prompt = custom_template.replace("{question}", question)
+        else:
+            prompt = f"""Generate an explanation for the following question:
+
+            Question: {question}
+
+            Explanation:
+            """
+        response = agent.invoke(prompt, temperature=0.8, max_tokens=150).strip()
+        explanations.append(response)
+
+        # Update progress bar
+        progress_bar.progress((i + 1) / total_questions)
+
+    return explanations
+
+
+# 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():
+    def password_entered():
+        if password_input == os.getenv('PASSWORD'):
+            st.session_state['password_correct'] = True
+        else:
+            st.error("Incorrect Password, please try again.")
+
+    password_input = st.text_input("Enter Password:", type="password")
+    submit_button = st.button("Submit", on_click=password_entered)
+
+    if submit_button and not st.session_state.get('password_correct', False):
+        st.error("Please enter a valid password to access the demo.")
+
+
+# Title of the application
+st.title('Natural Language Explanation Generation')
+
+# Sidebar description of the application
+st.sidebar.write("""
+### Welcome to the Natural Language Explanation Generation Demo
+This application allows you to generate high-quality explanations for various questions using different templates. Upload a CSV of questions, select an explanation template, and generate explanations.
+""")
+
+# Check if password has been validated
+if not st.session_state.get('password_correct', False):
+    check_password()
+else:
+    st.sidebar.success("Password Verified. Proceed with the demo.")
+
+    st.write("""
+    ### Instructions for Uploading CSV
+    Please upload a CSV file with the following column:
+    - `question`: The question you want explanations for.
+
+    **Example CSV Format:**
+    """)
+
+    # Display an example DataFrame
+    example_data_gen = {
+        "question": [
+            "What causes rainbows to appear in the sky?",
+            "Why is the sky blue?"
+        ]
+    }
+    example_df_gen = pd.DataFrame(example_data_gen)
+    st.dataframe(example_df_gen)
+
+    uploaded_file_gen = st.file_uploader("Upload CSV file with 'question' column", type=['csv'])
+
+    if uploaded_file_gen is not None:
+        template = st.selectbox("Select an explanation template", ["Default", "Chain of Thought", "Custom"])
+        model_name = st.selectbox('Select a model:', ['gpt4-1106', 'gpt35-1106'])
+
+        custom_template = ""
+        if template == "Custom":
+            custom_template = st.text_area("Enter your custom template",
+                                           value="Generate an explanation for the following question:\n\nQuestion: {question}\n\nExplanation:")
+
+        if st.button('Generate Explanations'):
+            questions_df = pd.read_csv(uploaded_file_gen)
+            questions = questions_df['question'].tolist()
+            explanations = generate_explanations(model_name, questions, template, custom_template)
+
+            result_df_gen = pd.DataFrame({
+                'question': questions,
+                'explanation': explanations
+            })
+
+            st.write('### Generated Explanations')
+            st.dataframe(result_df_gen)
+
+            # Create a CSV download link
+            csv_gen = result_df_gen.to_csv(index=False)
+            st.download_button(
+                label="Download generated explanations as CSV",
+                data=csv_gen,
+                file_name='generated_explanations.csv',
+                mime='text/csv',
+            )