Create app.py

app.py

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+import os
+import streamlit as st
+import openai
+import pandas as pd
+import time
+from typing import List, Tuple
+from uuid import uuid4
+
+# 🔑 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 = []
+        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 rationalize(self, problem: str, correct_answer: str) -> Tuple[str, str]:
+        """
+        🧐 Generates a rationale for a given problem using the correct answer as a hint.
+        """
+        prompt = self.construct_prompt(problem, include_answer=True, answer=correct_answer)
+        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 during rationalization: {e}")
+            return "", ""
+
+    def fine_tune_model(self):
+        """
+        🛠️ Fine-tunes the model on the generated rationales.
+        This is a placeholder function as fine-tuning would require
+        training a new model which is beyond the scope of this app.
+        """
+        # 🔄 In actual implementation, you would prepare the training data
+        # and use OpenAI's fine-tuning API or other methods to fine-tune
+        # the model. For demonstration, we'll just simulate the process.
+        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
+
+# 🖥️ Streamlit App
+def main():
+    st.title("🤖 Self-Taught Reasoner (STaR) Demonstration")
+    st.write("""
+    This app demonstrates the **Self-Taught Reasoner (STaR)** workflow. Enter problems to solve, and see how the model generates rationales, filters correct answers, and fine-tunes itself iteratively.
+    """)
+
+    # 🧩 Initialize the Self-Taught Reasoner
+    if 'star' not in st.session_state:
+        st.session_state.star = SelfTaughtReasoner()
+
+    star = st.session_state.star
+
+    # 📚 Section to add few-shot prompt examples
+    st.header("🔹 Step 1: Add Few-Shot Prompt Examples")
+    st.write("Provide a few examples with problems, rationales, and answers to bootstrap the reasoning process.")
+
+    with st.form(key='prompt_form'):
+        example_problem = st.text_area("📝 Example Problem", height=100)
+        example_rationale = st.text_area("🧠 Example Rationale", height=150)
+        example_answer = st.text_input("✅ Example Answer")
+        submit_example = st.form_submit_button("➕ Add Example")
+
+    if submit_example:
+        if not example_problem or not example_rationale or not example_answer:
+            st.warning("⚠️ Please fill in all fields to add an example.")
+        else:
+            star.add_prompt_example(example_problem, example_rationale, example_answer)
+            st.success("🎉 Example added.")
+
+    if star.prompt_examples:
+        st.subheader("📌 Current Prompt Examples:")
+        for idx, example in enumerate(star.prompt_examples):
+            st.write(f"**📚 Example {idx + 1}:**")
+            st.markdown(f"**Problem:**\n{example['Problem']}")
+            st.markdown(f"**Rationale:**\n{example['Rationale']}")
+            st.markdown(f"**Answer:**\n{example['Answer']}")
+
+    # 🔍 Section to input dataset
+    st.header("🔹 Step 2: Input Dataset")
+    st.write("Provide a dataset of problems and correct answers for the STaR process.")
+
+    dataset_input_method = st.radio("📥 How would you like to input the dataset?", ("Manual Entry", "Upload CSV"))
+
+    if dataset_input_method == "Manual Entry":
+        with st.form(key='dataset_form'):
+            dataset_problems = st.text_area("📝 Enter problems and answers in the format 'Problem | Answer', one per line.", height=200)
+            submit_dataset = st.form_submit_button("📤 Submit Dataset")
+
+        if submit_dataset:
+            if not dataset_problems:
+                st.warning("⚠️ Please enter at least one problem and answer.")
+            else:
+                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()})
+                    else:
+                        st.error(f"❌ Invalid format in line: {line}")
+                if dataset:
+                    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:
+            try:
+                st.session_state.dataset = pd.read_csv(uploaded_file)
+                if 'Problem' not in st.session_state.dataset.columns or 'Answer' not in st.session_state.dataset.columns:
+                    st.error("❌ CSV must contain 'Problem' and 'Answer' columns.")
+                    del st.session_state.dataset
+                else:
+                    st.success("✅ Dataset loaded.")
+            except Exception as e:
+                st.error(f"❌ Error loading CSV: {e}")
+
+    if 'dataset' in st.session_state:
+        st.subheader("📊 Current Dataset:")
+        st.dataframe(st.session_state.dataset.head())
+
+        # 🏃‍♂️ Section to run the STaR process
+        st.header("🔹 Step 3: Run STaR Process")
+        num_iterations = st.number_input("🔢 Number of Iterations to Run:", min_value=1, max_value=10, value=1)
+        run_star = st.button("🚀 Run STaR")
+
+        if run_star:
+            if not star.prompt_examples:
+                st.warning("⚠️ Please add at least one prompt example before running STaR.")
+            elif not openai.api_key:
+                st.warning("⚠️ OpenAI API key not found. Please set the `OPENAI_API_KEY` environment variable.")
+            else:
+                for _ in range(num_iterations):
+                    star.run_iteration(st.session_state.dataset)
+
+                st.header("📈 Results")
+                st.subheader("🧾 Generated Data")
+                st.dataframe(star.generated_data)
+
+                st.subheader("🧩 Rationalized Data")
+                st.dataframe(star.rationalized_data)
+
+                st.write("🔄 The model has been fine-tuned iteratively. You can now test it with new problems.")
+
+    # 🧪 Section to test the fine-tuned model
+    st.header("🔹 Step 4: Test the Fine-Tuned Model")
+    test_problem = st.text_area("📝 Enter a new problem to solve:", height=100)
+    test_button = st.button("✅ Solve Problem")
+
+    if test_button:
+        if not test_problem:
+            st.warning("⚠️ Please enter a problem to solve.")
+        elif not star.fine_tuned_model:
+            st.warning("⚠️ The model has not been fine-tuned yet. Please run the STaR process first.")
+        else:
+            # 🤖 For demonstration, we'll use the same generate_rationale_and_answer function
+            # In actual implementation, you would use the fine-tuned model
+            st.write("🔄 Generating rationale and answer using the fine-tuned model...")
+            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()