backup3.app.py

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
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")

    # 🧩 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: Run 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)
        if st.button("Run STaR"):
            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.")

    # Step 4: 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)
    if st.button("Solve Problem"):
        if not test_problem:
            st.warning("Please enter 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)

if __name__ == "__main__":
    main()