Update app.py

app.py

@@ -2,9 +2,9 @@ import os
 import streamlit as st
 import openai
 import pandas as pd
-import time
-from typing import List, Tuple  # Add Tuple import here
+from typing import List, Tuple
 from uuid import uuid4
+import time
 
 # 🔑 Set the OpenAI API key from an environment variable
 openai.api_key = os.getenv("OPENAI_API_KEY")
@@ -15,30 +15,11 @@ def get_session_id():
         st.session_state.session_id = str(uuid4())
     return st.session_state.session_id
 
-# 📚 Predefined examples loaded from Python dictionaries
-EXAMPLES = [
-    {
-        'Problem': 'What is deductive reasoning?',
-        'Rationale': 'Deductive reasoning starts from general premises to arrive at a specific conclusion.',
-        'Answer': 'It involves deriving specific conclusions from general premises.'
-    },
-    {
-        'Problem': 'What is inductive reasoning?',
-        'Rationale': 'Inductive reasoning involves drawing generalizations based on specific observations.',
-        'Answer': 'It involves forming general rules from specific examples.'
-    },
-    {
-        'Problem': 'Explain abductive reasoning.',
-        'Rationale': 'Abductive reasoning finds the most likely explanation for incomplete observations.',
-        'Answer': 'It involves finding the best possible explanation.'
-    }
-]
-
 # 🧠 STaR Algorithm Implementation
 class SelfTaughtReasoner:
     def __init__(self, model_engine="text-davinci-003"):
         self.model_engine = model_engine
-        self.prompt_examples = EXAMPLES  # Initialize with predefined examples
+        self.prompt_examples = []  # Initialize with an empty list
         self.iterations = 0
         self.generated_data = pd.DataFrame(columns=['Problem', 'Rationale', 'Answer', 'Is_Correct'])
         self.rationalized_data = pd.DataFrame(columns=['Problem', 'Rationale', 'Answer', 'Is_Correct'])
@@ -150,6 +131,19 @@ class SelfTaughtReasoner:
         self.fine_tune_model()
         self.iterations += 1
 
+# Predefined problem and answer list
+EXAMPLE_PROBLEM_ANSWERS = [
+    {"Problem": "What is deductive reasoning?", "Answer": "It is a logical process that draws specific conclusions from general principles."},
+    {"Problem": "What is inductive reasoning?", "Answer": "It is reasoning that forms general principles from specific examples."},
+    {"Problem": "Explain abductive reasoning.", "Answer": "It involves finding the best explanation for incomplete observations."},
+    {"Problem": "What is the capital of France?", "Answer": "Paris."},
+    {"Problem": "Who wrote Hamlet?", "Answer": "William Shakespeare."}
+]
+
+# Convert the example list into 'Problem | Answer' format
+def format_examples_for_text_area(examples):
+    return '\n'.join([f"{example['Problem']} | {example['Answer']}" for example in examples])
+
 # 🖥️ Streamlit App
 def main():
     st.title("🤖 Self-Taught Reasoner (STaR) Demonstration")
@@ -160,64 +154,47 @@ def main():
 
     star = st.session_state.star
 
-    # 📐 Wide format layout
-    col1, col2 = st.columns([1, 2])  # Column widths: col1 for input, col2 for display
-
     # Step 1: Few-Shot Prompt Examples
-    with col1:
-        st.header("Step 1: Add Few-Shot Prompt Examples")
-        st.write("Choose an example from the dropdown or input your own.")
-
-        selected_example = st.selectbox(
-            "Select a predefined example",
-            [f"Example {i + 1}: {ex['Problem']}" for i, ex in enumerate(EXAMPLES)]
-        )
+    st.header("Step 1: Add Few-Shot Prompt Examples")
+    st.write("Choose an example from the dropdown or input your own.")
 
-        # Prefill with selected example
-        example_idx = int(selected_example.split(" ")[1].replace(":", "")) - 1
-        example_problem = EXAMPLES[example_idx]['Problem']
-        example_rationale = EXAMPLES[example_idx]['Rationale']
-        example_answer = EXAMPLES[example_idx]['Answer']
+    selected_example = st.selectbox(
+        "Select a predefined example",
+        [f"Example {i + 1}: {ex['Problem']}" for i, ex in enumerate(EXAMPLE_PROBLEM_ANSWERS)]
+    )
 
-        st.text_area("Problem", value=example_problem, height=50, key="example_problem")
-        st.text_area("Rationale", value=example_rationale, height=100, key="example_rationale")
-        st.text_input("Answer", value=example_answer, key="example_answer")
+    # Prefill with selected example
+    example_idx = int(selected_example.split(" ")[1].replace(":", "")) - 1
+    example_problem = EXAMPLE_PROBLEM_ANSWERS[example_idx]['Problem']
+    example_answer = EXAMPLE_PROBLEM_ANSWERS[example_idx]['Answer']
 
-        if st.button("Add Example"):
-            star.add_prompt_example(st.session_state.example_problem, st.session_state.example_rationale, st.session_state.example_answer)
-            st.success("Example added successfully!")
+    st.text_area("Problem", value=example_problem, height=50, key="example_problem")
+    st.text_input("Answer", value=example_answer, key="example_answer")
 
-    with col2:
-        # Display current prompt examples
-        if star.prompt_examples:
-            st.subheader("Current Prompt Examples:")
-            for idx, example in enumerate(star.prompt_examples):
-                st.write(f"**Example {idx + 1}:**")
-                st.write(f"Problem: {example['Problem']}")
-                st.write(f"Rationale: {example['Rationale']}")
-                st.write(f"Answer: {example['Answer']}")
+    if st.button("Add Example"):
+        star.add_prompt_example(st.session_state.example_problem, "Rationale placeholder", st.session_state.example_answer)
+        st.success("Example added successfully!")
 
-    # Step 2: Input Dataset
+    # Step 2: Input Dataset (Problem | Answer format)
     st.header("Step 2: Input Dataset")
-    dataset_input_method = st.radio("How would you like to input the dataset?", ("Manual Entry", "Upload CSV"))
 
-    if dataset_input_method == "Manual Entry":
-        dataset_problems = st.text_area("Enter problems and answers in the format 'Problem | Answer', one per line.", height=200)
-        if st.button("Submit Dataset"):
-            dataset = []
-            lines = dataset_problems.strip().split('\n')
-            for line in lines:
-                if '|' in line:
-                    problem, answer = line.split('|', 1)
-                    dataset.append({'Problem': problem.strip(), 'Answer': answer.strip()})
-            st.session_state.dataset = pd.DataFrame(dataset)
-            st.success("Dataset loaded.")
-
-    else:
-        uploaded_file = st.file_uploader("Upload a CSV file with 'Problem' and 'Answer' columns.", type=['csv'])
-        if uploaded_file:
-            st.session_state.dataset = pd.read_csv(uploaded_file)
-            st.success("Dataset loaded.")
+    # Provide examples in the format 'Problem | Answer' as a default
+    prefilled_data = format_examples_for_text_area(EXAMPLE_PROBLEM_ANSWERS)
+    dataset_problems = st.text_area(
+        "Enter problems and answers in the format 'Problem | Answer', one per line.",
+        value=prefilled_data,
+        height=200
+    )
+
+    if st.button("Submit Dataset"):
+        dataset = []
+        lines = dataset_problems.strip().split('\n')
+        for line in lines:
+            if '|' in line:
+                problem, answer = line.split('|', 1)
+                dataset.append({'Problem': problem.strip(), 'Answer': answer.strip()})
+        st.session_state.dataset = pd.DataFrame(dataset)
+        st.success("Dataset loaded.")
 
     if 'dataset' in st.session_state:
         st.subheader("Current Dataset:")
@@ -252,14 +229,5 @@ def main():
             st.subheader("Answer:")
             st.write(answer)
 
-    # Footer with custom HTML/JS component
-    st.markdown("---")
-    st.write("Developed as a demonstration of the STaR method with enhanced Streamlit capabilities.")
-    st.components.v1.html("""
-        <div style="text-align: center; margin-top: 20px;">
-            <h3>🚀 Boost Your AI Reasoning with STaR! 🚀</h3>
-        </div>
-    """)
-
 if __name__ == "__main__":
     main()