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()