Update app/main.py

app/main.py

@@ -2,6 +2,9 @@ import streamlit as st
 import os
 import glob
 import sys
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.linear_model import LinearRegression
 
 # Add the parent directory to the Python path
 sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
@@ -114,7 +117,159 @@ def sidebar_navigation():
         for week in range(1, 11):
             if st.button(f"Week {week}", key=f"week_{week}"):
                 st.session_state.current_week = week
-                st.switch_page(f"app/pages/{week}_Week_{week}.py")
+                st.rerun()
+
+def show_week_content():
+    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.")
 
 # Main content
 def main():
@@ -125,33 +280,34 @@ def main():
         return
     
     # User is logged in, show course content
-    st.title("Data Science Research Paper Course")
-    
-    # Welcome message
-    st.markdown("""
-    ## Welcome to the Data Science Research Paper Course! 📚
-    
-    This 10-week course will guide you through the process of creating a machine learning research paper.
-    Each week, you'll learn new concepts and complete tasks that build upon each other.
-    
-    ### Getting Started
-    1. Use the sidebar to navigate between weeks
-    2. Complete the weekly tasks and assignments
-    3. Track your progress using the progress bar
-    4. Submit your work for feedback
-    
-    ### Course Overview
-    - Week 1: Research Topic Selection and Literature Review
-    - Week 2: Data Collection and Preprocessing
-    - Week 3: Exploratory Data Analysis
-    - Week 4: Feature Engineering
-    - Week 5: Model Selection and Baseline
-    - Week 6: Model Training and Optimization
-    - Week 7: Model Evaluation
-    - Week 8: Results Analysis
-    - Week 9: Paper Writing
-    - Week 10: Final Review and Submission
-    """)
+    if st.session_state.current_week == 1:
+        show_week_content()
+    else:
+        st.title("Data Science Research Paper Course")
+        st.markdown("""
+        ## Welcome to the Data Science Research Paper Course! 📚
+        
+        This 10-week course will guide you through the process of creating a machine learning research paper.
+        Each week, you'll learn new concepts and complete tasks that build upon each other.
+        
+        ### Getting Started
+        1. Use the sidebar to navigate between weeks
+        2. Complete the weekly tasks and assignments
+        3. Track your progress using the progress bar
+        4. Submit your work for feedback
+        
+        ### Course Overview
+        - Week 1: Research Topic Selection and Literature Review
+        - Week 2: Data Collection and Preprocessing
+        - Week 3: Exploratory Data Analysis
+        - Week 4: Feature Engineering
+        - Week 5: Model Selection and Baseline
+        - Week 6: Model Training and Optimization
+        - Week 7: Model Evaluation
+        - Week 8: Results Analysis
+        - Week 9: Paper Writing
+        - Week 10: Final Review and Submission
+        """)
 
 if __name__ == "__main__":
     load_css()