import streamlit as st
from sklearn.linear_model import LogisticRegression
import torch
from transformers import pipeline


def run():
    st.title("7. Machine Learning, Deep Learning & Transformers")
    st.write("## Overview")
    st.write("Learn about different machine learning models, deep learning models, and transformers.")

    st.write("## Key Concepts & Explanations")
    st.markdown("""
    - **Machine Learning Models**: Supervised, unsupervised, and reinforcement learning.  
    - **Deep Learning**: Neural networks with many layers, used for complex tasks like image recognition.
    - **Transformers**: A powerful model architecture used in natural language processing (NLP) tasks.
    """)

    # ML Example: Logistic Regression
    st.write("### Example: Logistic Regression")
    st.write("We'll use logistic regression to classify some sample data.")
    model = LogisticRegression()
    # (Insert a sample dataset and training procedure here)


    # Deep Learning Example: Using Pretrained Transformers
    st.write("### Example: Transformer Model for Sentiment Analysis")

    # Initialize the NLP pipeline for sentiment analysis
    nlp_pipeline = pipeline("sentiment-analysis")

    def analyze_sentiment(user_sentence):
        response = nlp_pipeline(user_sentence)
        return response[0]

    # Example usage
    user_sentence = st.text_input("Enter a sentence for sentiment analysis:")
    if user_sentence:
        response = analyze_sentiment(user_sentence)
        st.write(f"Sentiment: {response['label']}, Score: {response['score']}")


    # Deep Learning Example: Using Pretrained Transformers
    #st.write("### Example: Transformer Model")
    #nlp = pipeline("sentiment-analysis")
    #st.write(nlp("I love machine learning!"))

    st.write("## Quiz: Conceptual Questions")
    q1 = st.radio("What is a transformer model used for?", ["Text classification", "Image processing", "Time series analysis"])
    if q1 == "Text classification":
        st.success("✅ Correct!")
    else:
        st.error("❌ Incorrect.")

    st.write("## Code-Based Quiz")
    code_input = st.text_area("Write code to create a simple neural network using PyTorch", value="import torch\nimport torch.nn as nn\nclass SimpleNN(nn.Module):\n    def __init__(self):\n        super(SimpleNN, self).__init__()")
    if "super(SimpleNN" in code_input:
        st.success("✅ Correct!")
    else:
        st.error("❌ Try again.")

    st.write("## Learning Resources")
    st.markdown("""
    - 📚 [Deep Learning with PyTorch](https://pytorch.org/tutorials/)
    - 🌐 [Transformers Library Documentation](https://huggingface.co/docs/transformers/)
    """)