Update app.py

app.py

@@ -1,41 +1,57 @@
-# Import necessary libraries
 import pandas as pd
 import numpy as np
 import folium
 from sklearn.cluster import KMeans
 import gradio as gr
+from sklearn.preprocessing import StandardScaler
 
-# Load dataset
+# Function to load data from CSV file
 def load_data(file):
     data = pd.read_csv(file)
     return data
 
-# Function to process the data and apply clustering or regression model
+# Function to preprocess and apply clustering model to classify soil moisture
 def process_data(data):
-    X = data[['temperature', 'humidity', 'soil_type']]  # Example features
-    kmeans = KMeans(n_clusters=3)  # Example clustering model
-    data['moisture_category'] = kmeans.fit_predict(X)
+    # Preprocessing data (standardizing)
+    X = data[['temperature', 'humidity', 'soil_type']]  # Example feature columns
+    scaler = StandardScaler()
+    X_scaled = scaler.fit_transform(X)  # Scaling the data
+    
+    # Clustering model: KMeans (you can replace with regression models as well)
+    kmeans = KMeans(n_clusters=3, random_state=42)
+    data['moisture_category'] = kmeans.fit_predict(X_scaled)  # Classify into moisture categories (Low, Medium, High)
+    
     return data
 
-# Function to create an interactive map using folium
+# Function to create an interactive folium map
 def create_map(data):
+    # Initializing the map at the center of the provided data
     m = folium.Map(location=[data['latitude'].mean(), data['longitude'].mean()], zoom_start=12)
+    
+    # Plotting data points on the map
     for _, row in data.iterrows():
+        color = 'blue' if row['moisture_category'] == 0 else 'green' if row['moisture_category'] == 1 else 'red'
         folium.CircleMarker(location=[row['latitude'], row['longitude']],
                             radius=10,
-                            color='blue' if row['moisture_category'] == 0 else 'green' if row['moisture_category'] == 1 else 'red',
+                            color=color,
                             fill=True).add_to(m)
     return m
 
-# Gradio Interface
+# Main function to process the uploaded file, analyze data, and create the map
 def soil_moisture_mapping(file):
+    # Load data
     data = load_data(file)
+    
+    # Process data and apply the model
     processed_data = process_data(data)
+    
+    # Generate map with the results
     map_result = create_map(processed_data)
+    
     return map_result
 
-# Gradio Interface setup
-iface = gr.Interface(fn=soil_moisture_mapping, inputs="file", outputs="html")
+# Setting up the Gradio interface for file upload
+iface = gr.Interface(fn=soil_moisture_mapping, inputs="file", outputs="html", live=True)
 
-# Launch Gradio app
+# Launch the Gradio application
 iface.launch()