import streamlit as st
import numpy as np
import plotly.graph_objects as go
from sklearn.linear_model import LinearRegression

def show():
    st.markdown("""
    ## Week 1: Research Topic Selection and Literature Review
    
    This week, you'll learn how to:
    - Select a suitable research topic
    - Conduct a literature review
    - Define your research objectives
    - Create a research proposal
    """)
    
    # Topic Selection Section
    st.header("1. Topic Selection")
    st.markdown("""
    ### Guidelines for Selecting Your Research Topic:
    - Choose a topic that interests you
    - Ensure sufficient data availability
    - Consider the scope and complexity
    - Check for existing research gaps
    """)
    
    # Interactive Topic Selection
    st.subheader("Topic Selection Form")
    with st.form("topic_form"):
        research_area = st.selectbox(
            "Select your research area",
            ["Computer Vision", "NLP", "Time Series", "Recommendation Systems", "Other"]
        )
        
        topic = st.text_input("Proposed Research Topic")
        problem_statement = st.text_area("Brief Problem Statement")
        motivation = st.text_area("Why is this research important?")
        
        submitted = st.form_submit_button("Submit Topic")
        
        if submitted:
            st.success("Topic submitted successfully! We'll review and provide feedback.")
    
    # Linear Regression Visualization
    st.header("2. Linear Regression Demo")
    st.markdown("""
    ### Understanding Linear Regression
    
    Linear regression is a fundamental machine learning algorithm that models the relationship between a dependent variable and one or more independent variables.
    Below is an interactive demonstration of simple linear regression.
    """)
    
    # Create interactive controls
    col1, col2 = st.columns(2)
    with col1:
        n_points = st.slider("Number of data points", 10, 100, 50)
        noise = st.slider("Noise level", 0.1, 2.0, 0.5)
    with col2:
        slope = st.slider("True slope", -2.0, 2.0, 1.0)
        intercept = st.slider("True intercept", -5.0, 5.0, 0.0)
    
    # Generate synthetic data
    np.random.seed(42)
    X = np.random.rand(n_points) * 10
    y = slope * X + intercept + np.random.normal(0, noise, n_points)
    
    # Fit linear regression
    X_reshaped = X.reshape(-1, 1)
    model = LinearRegression()
    model.fit(X_reshaped, y)
    y_pred = model.predict(X_reshaped)
    
    # Create the plot
    fig = go.Figure()
    
    # Add scatter plot of actual data
    fig.add_trace(go.Scatter(
        x=X,
        y=y,
        mode='markers',
        name='Actual Data',
        marker=dict(color='blue')
    ))
    
    # Add regression line
    fig.add_trace(go.Scatter(
        x=X,
        y=y_pred,
        mode='lines',
        name='Regression Line',
        line=dict(color='red')
    ))
    
    # Update layout
    fig.update_layout(
        title='Linear Regression Visualization',
        xaxis_title='X',
        yaxis_title='Y',
        showlegend=True,
        height=500
    )
    
    # Display the plot
    st.plotly_chart(fig, use_container_width=True)
    
    # Display regression coefficients
    st.markdown(f"""
    ### Regression Results
    - Estimated slope: {model.coef_[0]:.2f}
    - Estimated intercept: {model.intercept_:.2f}
    - R² score: {model.score(X_reshaped, y):.2f}
    """)
    
    # Literature Review Section
    st.header("3. Literature Review")
    st.markdown("""
    ### Steps for Conducting Literature Review:
    1. Search for relevant papers
    2. Read and analyze key papers
    3. Identify research gaps
    4. Document your findings
    """)
    
    # Literature Review Template
    st.subheader("Literature Review Template")
    with st.expander("Download Template"):
        st.download_button(
            label="Download Literature Review Template",
            data="Literature Review Template\n\n1. Introduction\n2. Related Work\n3. Methodology\n4. Results\n5. Discussion\n6. Conclusion",
            file_name="literature_review_template.txt",
            mime="text/plain"
        )
    
    # Weekly Assignment
    st.header("Weekly Assignment")
    st.markdown("""
    ### Assignment 1: Research Proposal
    1. Select your research topic
    2. Write a brief problem statement
    3. Conduct initial literature review
    4. Submit your research proposal
    
    **Due Date:** End of Week 1
    """)
    
    # Assignment Submission
    st.subheader("Submit Your Assignment")
    with st.form("assignment_form"):
        proposal_file = st.file_uploader("Upload your research proposal (PDF or DOC)")
        comments = st.text_area("Additional comments or questions")
        
        if st.form_submit_button("Submit Assignment"):
            if proposal_file is not None:
                st.success("Assignment submitted successfully!")
            else:
                st.error("Please upload your research proposal.")

if __name__ == "__main__":
    show()