Create app.py

app.py

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+import os
+import streamlit as st
+import openai
+import pandas as pd
+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")
+
+# 🆔 Function to generate a unique session ID for caching
+def get_session_id():
+    if 'session_id' not in st.session_state:
+        st.session_state.session_id = str(uuid4())
+    return st.session_state.session_id
+
+# 🧠 STaR Algorithm Implementation
+class SelfTaughtReasoner:
+    def __init__(self, model_engine="text-davinci-003"):
+        self.model_engine = model_engine
+        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'])
+        self.fine_tuned_model = None  # 🏗️ Placeholder for fine-tuned model
+
+    def add_prompt_example(self, problem: str, rationale: str, answer: str):
+        """
+        ➕ Adds a prompt example to the few-shot examples.
+        """
+        self.prompt_examples.append({
+            'Problem': problem,
+            'Rationale': rationale,
+            'Answer': answer
+        })
+
+    def construct_prompt(self, problem: str, include_answer: bool = False, answer: str = "") -> str:
+        """
+        📝 Constructs the prompt for the OpenAI API call.
+        """
+        prompt = ""
+        for example in self.prompt_examples:
+            prompt += f"Problem: {example['Problem']}\n"
+            prompt += f"Rationale: {example['Rationale']}\n"
+            prompt += f"Answer: {example['Answer']}\n\n"
+
+        prompt += f"Problem: {problem}\n"
+        if include_answer:
+            prompt += f"Answer (as hint): {answer}\n"
+        prompt += "Rationale:"
+        return prompt
+
+    def generate_rationale_and_answer(self, problem: str) -> Tuple[str, str]:
+        """
+        🤔 Generates a rationale and answer for a given problem.
+        """
+        prompt = self.construct_prompt(problem)
+        try:
+            response = openai.Completion.create(
+                engine=self.model_engine,
+                prompt=prompt,
+                max_tokens=150,
+                temperature=0.7,
+                top_p=1,
+                frequency_penalty=0,
+                presence_penalty=0,
+                stop=["\n\n", "Problem:", "Answer:"]
+            )
+            rationale = response.choices[0].text.strip()
+            # 📝 Now generate the answer using the rationale
+            prompt += f" {rationale}\nAnswer:"
+            answer_response = openai.Completion.create(
+                engine=self.model_engine,
+                prompt=prompt,
+                max_tokens=10,
+                temperature=0,
+                top_p=1,
+                frequency_penalty=0,
+                presence_penalty=0,
+                stop=["\n", "\n\n", "Problem:"]
+            )
+            answer = answer_response.choices[0].text.strip()
+            return rationale, answer
+        except Exception as e:
+            st.error(f"❌ Error generating rationale and answer: {e}")
+            return "", ""
+
+    def fine_tune_model(self):
+        """
+        🛠️ Fine-tunes the model on the generated rationales.
+        """
+        time.sleep(1)  # ⏳ Simulate time taken for fine-tuning
+        self.fine_tuned_model = f"{self.model_engine}-fine-tuned-{get_session_id()}"
+        st.success(f"✅ Model fine-tuned: {self.fine_tuned_model}")
+
+    def run_iteration(self, dataset: pd.DataFrame):
+        """
+        🔄 Runs one iteration of the STaR process.
+        """
+        st.write(f"### Iteration {self.iterations + 1}")
+        progress_bar = st.progress(0)
+        total = len(dataset)
+        for idx, row in dataset.iterrows():
+            problem = row['Problem']
+            correct_answer = row['Answer']
+            # 🤖 Generate rationale and answer
+            rationale, answer = self.generate_rationale_and_answer(problem)
+            is_correct = (answer.lower() == correct_answer.lower())
+            # 📝 Record the generated data
+            self.generated_data = self.generated_data.append({
+                'Problem': problem,
+                'Rationale': rationale,
+                'Answer': answer,
+                'Is_Correct': is_correct
+            }, ignore_index=True)
+            # ❌ If incorrect, perform rationalization
+            if not is_correct:
+                rationale, answer = self.rationalize(problem, correct_answer)
+                is_correct = (answer.lower() == correct_answer.lower())
+                if is_correct:
+                    self.rationalized_data = self.rationalized_data.append({
+                        'Problem': problem,
+                        'Rationale': rationale,
+                        'Answer': answer,
+                        'Is_Correct': is_correct
+                    }, ignore_index=True)
+            progress_bar.progress((idx + 1) / total)
+        # 🔧 Fine-tune the model on correct rationales
+        st.write("🔄 Fine-tuning the model on correct rationales...")
+        self.fine_tune_model()
+        self.iterations += 1
+
+# Predefined problem and answer list for dataset
+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."}
+]
+
+# Additional problem set for testing fine-tuned model
+TEST_PROBLEM_SET = [
+    "What is the Pythagorean theorem?",
+    "Who developed the theory of relativity?",
+    "What is the main ingredient in bread?",
+    "Who is the author of 1984?",
+    "What is the boiling point of water?"
+]
+
+# 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")
+
+    # 🧩 Initialize the Self-Taught Reasoner
+    if 'star' not in st.session_state:
+        st.session_state.star = SelfTaughtReasoner()
+
+    star = st.session_state.star
+
+    # Step 1: Few-Shot Prompt Examples
+    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(EXAMPLE_PROBLEM_ANSWERS)]
+    )
+
+    # 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']
+
+    st.text_area("Problem", value=example_problem, height=50, key="example_problem")
+    st.text_input("Answer", value=example_answer, key="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 (Problem | Answer format)
+    st.header("Step 2: Input Dataset")
+
+    # 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:")
+        st.dataframe(st.session_state.dataset.head())
+
+        # Step 3: Test the Fine-Tuned Model (renamed from Step 4)
+        st.header("Step 3: Test the Fine-Tuned Model")
+
+        # Add dropdown for selecting a test problem
+        test_problem = st.selectbox(
+            "Select a problem to test the fine-tuned model",
+            TEST_PROBLEM_SET
+        )
+
+        if st.button("Solve Problem"):
+            if not test_problem:
+                st.warning("Please enter or select a problem to solve.")
+            else:
+                rationale, answer = star.generate_rationale_and_answer(test_problem)
+                st.subheader("Rationale:")
+                st.write(rationale)
+                st.subheader("Answer:")
+                st.write(answer)
+
+    # Footer
+    st.write("---")
+    st.write("Developed as a demonstration of the STaR method.")
+
+if __name__ == "__main__":
+    main()