Create code.py

code.py

@@ -0,0 +1,281 @@
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.ensemble import RandomForestClassifier
+import gradio as gr
+import matplotlib.pyplot as plt
+import seaborn as sns
+from io import BytesIO
+import requests
+
+# Load and preprocess data
+file_path = '/content/DSV Project Final Spreadsheet.csv'  
+data = pd.read_csv(file_path)
+
+
+# Convert string columns to numeric
+for col in ['N', 'P', 'K']:
+    data[col] = pd.to_numeric(data[col], errors='coerce')
+
+# Encode categorical variables
+le_soil = LabelEncoder()
+le_season = LabelEncoder()
+data['Soil Type'] = le_soil.fit_transform(data['Soil Type'])
+data['Season'] = le_season.fit_transform(data['Season'])
+
+# Features and target variables
+features = ['N', 'P', 'K', 'temperature', 'humidity', 'ph', 'rainfall', 'Soil Moisture Level (%)', 'Soil Type', 'Season']
+X = data[features]
+
+# Encode target variables
+le_crop = LabelEncoder()
+le_fertilizer = LabelEncoder()
+le_water_pump = LabelEncoder()
+le_fire = LabelEncoder()
+
+y_crop = le_crop.fit_transform(data['Crop Name'])
+y_fertilizer = le_fertilizer.fit_transform(data['Fertilizer Recommendations'])
+y_water_pump = le_water_pump.fit_transform(data['Water Pump Status (ON/OFF)'])
+y_fire = le_fire.fit_transform(data['Fire Detection (YES/NO)'])
+
+# Scale features
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+# Create models
+crop_model = RandomForestClassifier(n_estimators=100, random_state=42)
+crop_model.fit(X_scaled, y_crop)
+
+fertilizer_model = RandomForestClassifier(n_estimators=100, random_state=42)
+fertilizer_model.fit(X_scaled, y_fertilizer)
+
+water_pump_model = RandomForestClassifier(n_estimators=100, random_state=42)
+water_pump_model.fit(X_scaled, y_water_pump)
+
+fire_model = RandomForestClassifier(n_estimators=100, random_state=42)
+fire_model.fit(X_scaled, y_fire)
+
+# Prediction function for the UI
+def predict_crop(n, p, k, temperature, humidity, ph, rainfall, soil_moisture, soil_type, season):
+    # Check if soil_type and season are within bounds
+    if not (0 <= soil_type <= 4):
+        return {"Error": "Please enter a valid Soil Type (0 -> Alluvial, 1 -> Black, 2 -> Clayey, 3 -> Laterite, 4 -> Sandy)"}
+    
+    if not (0 <= season <= 3):
+        return {"Error": "Please enter a valid Season (0 -> Autumn, 1 -> Spring, 2 -> Summer, 3 -> Winter)"}
+
+    # Create input array
+    input_data = pd.DataFrame([[n, p, k, temperature, humidity, ph, rainfall, soil_moisture, soil_type, season]],
+                              columns=features)
+    input_scaled = scaler.transform(input_data)
+    
+    # Make predictions
+    crop_pred = le_crop.inverse_transform(crop_model.predict(input_scaled))[0]
+    fertilizer_pred = le_fertilizer.inverse_transform(fertilizer_model.predict(input_scaled))[0]
+    water_pump_pred = le_water_pump.inverse_transform(water_pump_model.predict(input_scaled))[0]
+    fire_pred = le_fire.inverse_transform(fire_model.predict(input_scaled))[0]
+    
+    return {
+        "Recommended Crop": crop_pred,
+        "Fertilizer Recommendation": fertilizer_pred,
+        "Water Pump Status": water_pump_pred,
+        "Fire Detection": fire_pred
+    }
+
+# Visualization function
+def visualize_predictions(n, p, k, temperature, humidity, ph, rainfall, soil_moisture, soil_type, season, show_crop, show_fertilizer, show_water_pump, show_fire):
+    input_data = pd.DataFrame([[n, p, k, temperature, humidity, ph, rainfall, soil_moisture, soil_type, season]],
+                              columns=features)
+    input_scaled = scaler.transform(input_data)
+    
+    # Predictions
+    crop_pred = le_crop.inverse_transform(crop_model.predict(input_scaled))[0]
+    fertilizer_pred = le_fertilizer.inverse_transform(fertilizer_model.predict(input_scaled))[0]
+    water_pump_pred = le_water_pump.inverse_transform(water_pump_model.predict(input_scaled))[0]
+    fire_pred = le_fire.inverse_transform(fire_model.predict(input_scaled))[0]
+
+    # Plot
+    fig, ax = plt.subplots()
+    
+    labels = []
+    values = []
+    
+    if show_crop:
+        labels.append("Recommended Crop")
+        values.append(crop_pred)
+    if show_fertilizer:
+        labels.append("Fertilizer")
+        values.append(fertilizer_pred)
+    if show_water_pump:
+        labels.append("Water Pump Status")
+        values.append(water_pump_pred)
+    if show_fire:
+        labels.append("Fire Detection")
+        values.append(fire_pred)
+    
+    ax.barh(labels, [1]*len(labels), color=['#4CAF50', '#FF9800', '#2196F3', '#F44336'])
+    
+    for i, v in enumerate(values):
+        ax.text(1.05, i, str(v), color='black', fontweight='bold')
+
+    ax.set_xlim(0, 1.5)
+    
+    plt.tight_layout()
+    return fig
+
+# Data Insight function
+def data_insight(visualization_type, predicted_outcome, input1, input2, input3):
+    plt.figure(figsize=(12, 10))
+    
+    if visualization_type == "Scatter Plot":
+        if predicted_outcome == "Recommended Crop":
+            target = data['Crop Name']
+        elif predicted_outcome == "Fertilizer Recommendation":
+            target = data['Fertilizer Recommendations']
+        elif predicted_outcome == "Water Pump Status":
+            target = data['Water Pump Status (ON/OFF)']
+        else:
+            target = data['Fire Detection (YES/NO)']
+        
+        plt.scatter(data[input1], data[input2], c=data[input3], cmap='viridis', alpha=0.6)
+        plt.colorbar(label=input3)
+        plt.xlabel(input1)
+        plt.ylabel(input2)
+        plt.title(f"Scatter Plot: {predicted_outcome}\n{input1} vs {input2}, color intensity: {input3}")
+    
+    elif visualization_type == "Correlation Plot":
+        numeric_data = data[features + ['Soil Moisture Level (%)']]
+        corr = numeric_data.corr()
+        sns.heatmap(corr, annot=True, cmap="coolwarm", fmt=".2f")
+        plt.title("Correlation Plot of Numeric Features")
+    
+    elif visualization_type == "Pair Plot":
+        sns.pairplot(data[features], diag_kind="kde")
+        plt.suptitle("Pair Plot of Input Features", y=1.02)
+    
+    elif visualization_type == "Box Plot":
+        plt.figure(figsize=(15, 6))
+        sns.boxplot(data=data[features])
+        plt.title("Box Plot of Input Features")
+        plt.xticks(rotation=45)
+    
+    elif visualization_type == "Violin Plot":
+        plt.figure(figsize=(15, 6))
+        sns.violinplot(data=data[features])
+        plt.title("Violin Plot of Input Features")
+        plt.xticks(rotation=45)
+    
+    plt.tight_layout()
+    return plt
+
+# Function to update input options based on predicted outcome
+def update_input_options(predicted_outcome):
+    if predicted_outcome == "Recommended Crop":
+        return gr.update(choices=features, value=features[0]), gr.update(choices=features, value=features[1]), gr.update(choices=features, value=features[2])
+    elif predicted_outcome == "Fertilizer Recommendation":
+        options = ['N', 'P', 'K', 'ph']
+        return gr.update(choices=options, value=options[0]), gr.update(choices=options, value=options[1]), gr.update(choices=options, value=options[2])
+    elif predicted_outcome == "Water Pump Status":
+        options = ['Soil Moisture Level (%)', 'temperature', 'humidity', 'Season']
+        return gr.update(choices=options, value=options[0]), gr.update(choices=options, value=options[1]), gr.update(choices=options, value=options[2])
+    else:  # Fire Detection
+        options = ['temperature', 'humidity', 'Season']
+        return gr.update(choices=options, value=options[0]), gr.update(choices=options, value=options[1]), gr.update(choices=options, value=options[2])
+
+# Create the Gradio UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🌱 Smart Agriculture Prediction System")
+    
+    with gr.Row():
+        with gr.Column():
+            n = gr.Number(label="Nitrogen (N) [kg/ha]" ,info="Range: 0-100")
+            p = gr.Number(label="Phosphorus (P) [kg/ha]" ,info="Range: 30-90")
+            k = gr.Number(label="Potassium (K) [kg/ha]" ,info="Range: 15-85")
+            temperature = gr.Number(label="Temperature [°C]",info="Range: 15-40")
+        with gr.Column():
+            humidity = gr.Number(label="Humidity [%]",info="Range: 10-85")
+            ph = gr.Number(label="pH Level" ,info="Range: 4-9")
+            rainfall = gr.Number(label="Rainfall [mm]",info="Range: 30-300")
+            soil_moisture = gr.Number(label="Soil Moisture Level (%)",info="Range: 55-75")
+    
+    soil_type = gr.Number(label="Soil Type [0->Alluvial 1->Black 2->Clayey 3->Laterite 4->Sandy]")
+    season = gr.Number(label="Season [0->Autumn 1->Spring 2->Summer 3->Winter]")
+    
+    analyze_btn = gr.Button("Predict", variant="primary")
+    
+    with gr.TabItem("Predictions"):
+        prediction_output = gr.JSON()
+
+    # Visualization options
+    with gr.TabItem("Visualization"):
+        show_crop = gr.Checkbox(label="Show Recommended Crop", value=True)
+        show_fertilizer = gr.Checkbox(label="Show Fertilizer Recommendation", value=True)
+        show_water_pump = gr.Checkbox(label="Show Water Pump Status", value=True)
+        show_fire = gr.Checkbox(label="Show Fire Detection", value=True)
+        
+        visualize_btn = gr.Button("Visualize", variant="secondary")
+        plot_output = gr.Plot()
+
+    # Data Insight options
+    with gr.TabItem("Data Insight"):
+        visualization_type = gr.Radio(["Scatter Plot", "Correlation Plot", "Pair Plot", "Box Plot", "Violin Plot"], label="Visualization Type")
+        predicted_outcome = gr.Radio(["Recommended Crop", "Fertilizer Recommendation", "Water Pump Status", "Fire Detection"], label="Predicted Outcome", visible=False)
+        input1 = gr.Dropdown(choices=features, label="X-axis", visible=False)
+        input2 = gr.Dropdown(choices=features, label="Y-axis", visible=False)
+        input3 = gr.Dropdown(choices=features, label="Color intensity", visible=False)
+        
+        insight_btn = gr.Button("Generate Insight", variant="secondary")
+        insight_plot = gr.Plot()
+
+        def update_insight_inputs(viz_type):
+            if viz_type == "Scatter Plot":
+                return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
+            else:
+                return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+
+        visualization_type.change(update_insight_inputs, inputs=[visualization_type], outputs=[predicted_outcome, input1, input2, input3])
+        predicted_outcome.change(update_input_options, inputs=[predicted_outcome], outputs=[input1, input2, input3])
+
+    # Adding examples to the UI
+    gr.Examples(
+        examples=[
+            [90, 60, 50, 25, 65, 6.5, 200, 65, 1, 0],  # Example 1: Black soil, Autumn season
+            [40, 45, 30, 30, 50, 7.0, 100, 70, 4, 2]   # Example 2: Sandy soil, Summer season
+        ],
+        inputs=[n, p, k, temperature, humidity, ph, rainfall, soil_moisture, soil_type, season],
+        outputs=prediction_output,
+        label="Try these examples"
+    )
+
+    analyze_btn.click(predict_crop, 
+                      inputs=[n, p, k, temperature, humidity, ph, rainfall, soil_moisture, soil_type, season],
+                      outputs=prediction_output)
+
+    visualize_btn.click(visualize_predictions, 
+                        inputs=[n, p, k, temperature, humidity, ph, rainfall, soil_moisture, soil_type, season, 
+                                show_crop, show_fertilizer, show_water_pump, show_fire],
+                        outputs=plot_output)
+
+    insight_btn.click(data_insight,
+                      inputs=[visualization_type, predicted_outcome, input1, input2, input3],
+                      outputs=insight_plot)
+    
+    # Add Footer
+    gr.HTML("""
+        <div style='
+            background-color: #333; 
+            color: #f0f0f0; 
+            border-radius: 8px; 
+            padding: 20px; 
+            margin-top: 40px;
+            text-align: center; 
+            font-size: 16px;
+            font-family: "Helvetica", sans-serif;'>
+            <strong>Created By Harsh Jain [23/IT/062] & Daksh Yadav [23/IT/048]</strong>
+            <br>
+            <span style='font-size: 14px; color: #b0b0b0;'>Smart Agriculture Prediction System</span>
+        </div>
+    """)
+
+if __name__ == "__main__":
+    demo.launch()