Update pages/EDA.py

pages/EDA.py

@@ -5,7 +5,7 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 
 st.markdown(
-    "<h3 style='text-align: left; color: #555; font-size: 23px;'>Data Frame</h3>",
+    "<h3 style='text-align: left; color: #555; font-size: 24px;'>Data Frame</h3>",
     unsafe_allow_html=True
 )
 
@@ -20,10 +20,10 @@ df = pd.read_csv("https://huggingface.co/spaces/varshitha22/Crop_Recommendation/
 num_cols = ['Nitrogen', 'Phosphorus', 'Potassium', 'Temperature', 'Humidity', 'pH_Value', 'Rainfall']
 
 # Title
-st.markdown("<h2 style='text-align: left; color: #2E86C1;font-size: 23px;'>Outlier Detection and Handling</h2>", unsafe_allow_html=True)
+st.markdown("<h2 style='text-align: left; color: #2E86C1;font-size: 24px;'>Outlier Detection and Handling</h2>", unsafe_allow_html=True)
 
 # --- Boxplot Visualization ---
-st.markdown("<h3 style='text-align: left; color: #D35400;font-size: 23px;'>Outlier Detection (Boxplots)</h3>", unsafe_allow_html=True)
+st.markdown("<h3 style='text-align: left; color: #D35400;font-size: 24px;'>Outlier Detection (Boxplots)</h3>", unsafe_allow_html=True)
 
 fig, ax = plt.subplots(2, 4, figsize=(12, 6))
 ax = ax.flatten()
@@ -36,7 +36,7 @@ plt.tight_layout()
 st.pyplot(fig)
 
 # --- Outlier Handling ---
-st.markdown("<h3 style='text-align: left; color: #28B463;font-size: 23px;'>Outlier Handling using IQR</h3>", unsafe_allow_html=True)
+st.markdown("<h3 style='text-align: left; color: #28B463;font-size: 24px;'>Outlier Handling using IQR</h3>", unsafe_allow_html=True)
 
 outlier_counts = {}
 
@@ -59,7 +59,7 @@ st.write("Number of Outliers Detected:")
 st.write(outlier_counts)
 
 # Title with color
-st.markdown("<h2 style='text-align: left; color: #2E86C1;font-size: 23px;'>Why Use the IQR Method?</h2>", unsafe_allow_html=True)
+st.markdown("<h2 style='text-align: left; color: #2E86C1;font-size: 24px;'>Why Use the IQR Method?</h2>", unsafe_allow_html=True)
 
 # Explanation with smaller font size
 st.markdown("""
@@ -81,7 +81,7 @@ st.markdown("""
 </div>
 """, unsafe_allow_html=True)
 
-st.markdown("<h2 style='text-align: left; color: #D35400;font-size: 23px;'>Crop Recommendation System - Feature Visualization</h2>", unsafe_allow_html=True)
+st.markdown("<h2 style='text-align: left; color: #D35400;font-size: 24px;'>Crop Recommendation System - Feature Visualization</h2>", unsafe_allow_html=True)
 # List of available visualizations
 visualizations = [
     "Nitrogen Requirement per Crop", 
@@ -151,7 +151,7 @@ ax.set_title("Proportion of Crops", fontsize=3)
 st.pyplot(fig)
 
 # Above-Average Nutrient Requirement Crops
-st.markdown("<h2 style='text-align: left; color: #D35400;font-size: 23px;'>Crops that require above-average soil nutrients</h2>", unsafe_allow_html=True)
+st.markdown("<h2 style='text-align: left; color: #D35400;font-size: 24px;'>Crops that require above-average soil nutrients</h2>", unsafe_allow_html=True)
 
 # Calculate average nutrient levels
 avg_nitrogen = df['Nitrogen'].mean()
@@ -180,7 +180,7 @@ more_avg_of_soil_nutrients = df[
 plot_nutrient_crops(more_avg_of_soil_nutrients, "Crops Growing in Nutrient-Rich Soil")
 
 # Insights for Nutrient-Rich Crops
-st.markdown("<h2 style='color: #2E86C1; font-size: 23px;'>Insights:</h2>", unsafe_allow_html=True)
+st.markdown("<h2 style='color: #2E86C1; font-size: 24px;'>Insights:</h2>", unsafe_allow_html=True)
 st.markdown("""
 <style>
     .insights-box {
@@ -200,7 +200,7 @@ st.markdown("""
 """, unsafe_allow_html=True)
 
 # Below-Average Nutrient Requirement Crops
-st.markdown("<h2 style='text-align: left; color: #D35400;font-size: 23px;'>Crops that require below-average soil nutrients</h2>", unsafe_allow_html=True)
+st.markdown("<h2 style='text-align: left; color: #D35400;font-size: 24px;'>Crops that require below-average soil nutrients</h2>", unsafe_allow_html=True)
 
 # Plot: Crops in Nutrient-Poor Soil
 less_avg_of_soil_nutrients = df[
@@ -212,7 +212,7 @@ less_avg_of_soil_nutrients = df[
 plot_nutrient_crops(less_avg_of_soil_nutrients, "Crops Growing in Nutrient-Poor Soil")
 
 # Insights for Nutrient-Poor Crops
-st.markdown("<h2 style='color: #D35400; font-size: 23px;'>Insights:</h2>", unsafe_allow_html=True)
+st.markdown("<h2 style='color: #D35400; font-size: 24px;'>Insights:</h2>", unsafe_allow_html=True)
 st.markdown("""
 <style>
     .insights-box {
@@ -230,8 +230,8 @@ st.markdown("""
     3. Unlike Banana and Rice, which need nutrient-rich soil to grow well, crops like Orange, Mango, and Coconut can survive with fewer nutrients. These fruit trees are naturally more resistant to dry conditions and can grow even in sandy or less fertile soils.<br>
 </div>
 """, unsafe_allow_html=True)
-st.markdown("<h2 style='color: #2E86C1; font-size: 23px;'>How This Helps Farmers:</h2>", unsafe_allow_html=True)
-st.markdown("<h2 style='color: #28B463; font-size: 20px;'>Choosing the Right Crops for Low-Fertility Soil:</h2>", unsafe_allow_html=True)
+st.markdown("<h2 style='color: #2E86C1; font-size: 24px;'>How This Helps Farmers:</h2>", unsafe_allow_html=True)
+st.markdown("<h2 style='color: #28B463; font-size: 22px;'>Choosing the Right Crops for Low-Fertility Soil:</h2>", unsafe_allow_html=True)
 st.markdown("""
 <style>
     .insights-box {
@@ -248,7 +248,7 @@ st.markdown("""
     2. Instead, they can plant fruit trees like Orange, Mango, and Coconut, which can survive and produce good yields even in poor soil.<br>
 </div>
 """, unsafe_allow_html=True)
-st.markdown("<h2 style='color: #28B463; font-size: 20px;'>Improving Soil Naturally with Legumes:</h2>", unsafe_allow_html=True)
+st.markdown("<h2 style='color: #28B463; font-size: 24px;'>Improving Soil Naturally with Legumes:</h2>", unsafe_allow_html=True)
 st.markdown("""
 <style>
     .insights-box {
@@ -273,7 +273,7 @@ st.markdown("""
 
 
 
-st.markdown("<h2 style='text-align: left; color: #D35400;font-size: 23px;'>Crops that require above-average climate factors</h2>", unsafe_allow_html=True)
+st.markdown("<h2 style='text-align: left; color: #D35400;font-size: 24px;'>Crops that require above-average climate factors</h2>", unsafe_allow_html=True)
 
 # Calculate average of climate factors
 avg_Temperature = df['Temperature'].mean()
@@ -473,7 +473,7 @@ fig, ax = plt.subplots(figsize=(6, 4))  # Reduced size
 sns.scatterplot(data=df, x="Rainfall", y="Crop", hue="Crop", palette="rainbow", legend=False, ax=ax)
 ax.set_xlabel("Rainfall (mm)", fontsize=8)
 ax.set_ylabel("Crop", fontsize=8)
-ax.set_title("Rainfall vs Crop Type", fontsize=5)
+ax.set_title("Rainfall vs Crop Type", fontsize=3)
 st.pyplot(fig)
 
 st.markdown("<h2 style='color: #D35400; font-size: 23px;'> Temperature vs. Humidity</h2>", unsafe_allow_html=True)
@@ -481,14 +481,14 @@ fig, ax = plt.subplots(figsize=(6, 4))  # Reduced size
 sns.scatterplot(data=df, x="Temperature", y="Humidity", alpha=0.6, color="blue", ax=ax)
 ax.set_xlabel("Temperature (°C)", fontsize=8)
 ax.set_ylabel("Humidity (%)", fontsize=8)
-ax.set_title("Temperature vs Humidity", fontsize=5)
+ax.set_title("Temperature vs Humidity", fontsize=3)
 st.pyplot(fig)
 
 st.markdown("<h2 style='color: #2E86C1; font-size: 23px;'> Correlation Heatmap of Soil & Climate Factors</h2>", unsafe_allow_html=True)
 corr_matrix = df[['Nitrogen', 'Phosphorus', 'Potassium', 'Temperature', 'Humidity', 'pH_Value', 'Rainfall']].corr()
 fig, ax = plt.subplots(figsize=(6, 4))  # Reduced size
 sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", ax=ax)
-ax.set_title("Correlation Heatmap", fontsize=5)
+ax.set_title("Correlation Heatmap", fontsize=3)
 st.pyplot(fig)
 
 st.markdown("<h2 style='color: #D35400; font-size: 23px;'> 🌾 Crop Recommendations by Season</h2>", unsafe_allow_html=True)