Update app.py

app.py

@@ -2,11 +2,9 @@ import gradio as gr
 import pandas as pd
 import joblib
 import numpy as np
-
-# Load the trained model
 import os
-import joblib
 
+# Load the trained model
 script_dir = os.path.dirname(os.path.abspath(__file__))
 model_path = os.path.join(script_dir, "district_yield_pipeline.pkl")
 
@@ -50,7 +48,9 @@ STATES = [
     'Uttar Pradesh', 'Uttarakhand', 'West Bengal'
 ]
 
-def predict_yield(crop, season, state, area, annual_rainfall, fertilizer, pesticide):
+YEARS = list(range(1997, 2020))
+
+def predict_yield(crop, season, state, area, annual_rainfall, fertilizer, pesticide, year):
     """
     Predict crop yield based on input features
     """
@@ -66,7 +66,8 @@ def predict_yield(crop, season, state, area, annual_rainfall, fertilizer, pestic
             'Area': [float(area)],
             'Annual_Rainfall': [float(annual_rainfall)],
             'Fertilizer': [float(fertilizer)],
-            'Pesticide': [float(pesticide)]
+            'Pesticide': [float(pesticide)],
+            'Year': [int(year)]
         })
         
         # Make prediction
@@ -88,6 +89,7 @@ def predict_yield(crop, season, state, area, annual_rainfall, fertilizer, pestic
         - 🌧️ **Annual Rainfall:** {annual_rainfall} mm
         - 💊 **Fertilizer:** {fertilizer} kg
         - 🧪 **Pesticide:** {pesticide} kg
+        - 📅 **Year:** {year}
         
         ---
         
@@ -127,14 +129,14 @@ def predict_yield(crop, season, state, area, annual_rainfall, fertilizer, pestic
 def load_example(example_name):
     """Load predefined examples"""
     examples = {
-        "Rice - Kharif Season": ("Rice", "Kharif", "West Bengal", 5000, 2000, 500000, 1000),
-        "Wheat - Rabi Season": ("Wheat", "Rabi", "Punjab", 3000, 1200, 400000, 800),
-        "Cotton - Kharif Season": ("Cotton(Lint)", "Kharif", "Gujarat", 4000, 800, 350000, 900),
-        "Sugarcane - Whole Year": ("Sugarcane", "Whole Year", "Maharashtra", 2500, 1500, 600000, 1200),
-        "Potato - Rabi Season": ("Potato", "Rabi", "Uttar Pradesh", 1500, 900, 250000, 600)
+        "Rice - Kharif Season": ("Rice", "Kharif", "West Bengal", 5000, 2000, 500000, 1000, 2015),
+        "Wheat - Rabi Season": ("Wheat", "Rabi", "Punjab", 3000, 1200, 400000, 800, 2015),
+        "Cotton - Kharif Season": ("Cotton(Lint)", "Kharif", "Gujarat", 4000, 800, 350000, 900, 2015),
+        "Sugarcane - Whole Year": ("Sugarcane", "Whole Year", "Maharashtra", 2500, 1500, 600000, 1200, 2015),
+        "Potato - Rabi Season": ("Potato", "Rabi", "Uttar Pradesh", 1500, 900, 250000, 600, 2015)
     }
     
-    return examples.get(example_name, ("Rice", "Kharif", "Karnataka", 1000, 1500, 100000, 500))
+    return examples.get(example_name, ("Rice", "Kharif", "Karnataka", 1000, 1500, 100000, 500, 2015))
 
 # Custom CSS for better styling
 custom_css = """
@@ -199,6 +201,13 @@ with gr.Blocks(css=custom_css, title="Crop Yield Prediction System") as demo:
                 info="Choose the state where the crop is grown"
             )
             
+            year_input = gr.Dropdown(
+                choices=YEARS,
+                label="📅 Select Year",
+                value=2015,
+                info="Select the year for prediction (1997-2019)"
+            )
+            
             # Numeric inputs
             area_input = gr.Number(
                 label="📏 Area (in hectares)",
@@ -228,8 +237,8 @@ with gr.Blocks(css=custom_css, title="Crop Yield Prediction System") as demo:
             with gr.Row():
                 predict_btn = gr.Button("🔮 Predict Yield", variant="primary", size="lg")
                 clear_btn = gr.ClearButton(
-                    components=[crop_input, season_input, state_input, area_input, 
-                               rainfall_input, fertilizer_input, pesticide_input],
+                    components=[crop_input, season_input, state_input, year_input, 
+                               area_input, rainfall_input, fertilizer_input, pesticide_input],
                     value="🔄 Clear"
                 )
             
@@ -270,16 +279,17 @@ with gr.Blocks(css=custom_css, title="Crop Yield Prediction System") as demo:
         - **Testing Accuracy:** 97.82% R² Score
         - **RMSE:** 122.72
         - **Dataset:** Indian Agricultural Crop Yield Data (1997-2019)
-        - **Features:** 7 input features including crop type, season, location, and farming inputs
+        - **Features:** 8 input features including crop type, season, location, year, and farming inputs
         
         ### How to Use
         
         1. Select the crop type from the dropdown
         2. Choose the appropriate growing season
         3. Select the state where cultivation occurs
-        4. Enter numerical values for area, rainfall, fertilizer, and pesticide
-        5. Click "Predict Yield" to get results
-        6. Review the prediction and insights provided
+        4. Select the year for prediction
+        5. Enter numerical values for area, rainfall, fertilizer, and pesticide
+        6. Click "Predict Yield" to get results
+        7. Review the prediction and insights provided
         
         ### Data Ranges (for reference)
         
@@ -287,6 +297,7 @@ with gr.Blocks(css=custom_css, title="Crop Yield Prediction System") as demo:
         - **Rainfall:** 300 - 6,500 mm/year
         - **Fertilizer:** 100 - 100,000,000 kg
         - **Pesticide:** 1 - 300,000 kg
+        - **Year:** 1997 - 2019
         
         ### Disclaimer
         
@@ -299,7 +310,7 @@ with gr.Blocks(css=custom_css, title="Crop Yield Prediction System") as demo:
     predict_btn.click(
         fn=predict_yield,
         inputs=[crop_input, season_input, state_input, area_input, 
-                rainfall_input, fertilizer_input, pesticide_input],
+                rainfall_input, fertilizer_input, pesticide_input, year_input],
         outputs=[prediction_output, insights_output]
     )
     
@@ -307,7 +318,7 @@ with gr.Blocks(css=custom_css, title="Crop Yield Prediction System") as demo:
         fn=load_example,
         inputs=[example_dropdown],
         outputs=[crop_input, season_input, state_input, area_input, 
-                 rainfall_input, fertilizer_input, pesticide_input]
+                 rainfall_input, fertilizer_input, pesticide_input, year_input]
     )
     
     # Footer