Upload 1_Week_1.py

app/pages/1_Week_1.py

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+import streamlit as st
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.linear_model import LinearRegression
+
+# Page configuration
+st.set_page_config(
+    page_title="Week 1 - Research Topic Selection",
+    page_icon="📚",
+    layout="wide"
+)
+
+# Check if user is logged in
+if not st.session_state.get("logged_in", False):
+    st.warning("Please log in to access this page.")
+    st.stop()
+
+# Main 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.")