Update app.py

app.py

@@ -1,47 +1,95 @@
 import gradio as gr
 import pandas as pd
-import joblib
 import pickle
+import numpy as np
 
-# Load the trained model
-with open('Fertilizer_recommender.pkl', 'rb') as f:
-    model = pickle.load(f)
+# Extract unique values from the dataset
+df = pd.read_csv('Fertilizer Prediction.csv')
 
-# Step 4: Define prediction function with data preprocessing
-def recommend_fertilizer(temperature, humidity, moisture, soil_type, crop_type, nitrogen, phosphorous, potassium):
-    # Example mappings for soil type and crop type (replace with your actual mappings)
-    soil_type_mapping = {"Sandy": 1, "Clay": 2, "Loam": 3}
-    crop_type_mapping = {"Wheat": 1, "Rice": 2, "Maize": 3}
+# Create mapping dataframes based on your dataset
+soil_types = df['Soil Type'].unique().tolist()
+soil_type_mapping = {soil: idx+1 for idx, soil in enumerate(sorted(soil_types))}
+Soil_Type = pd.DataFrame({'Encoded': list(soil_type_mapping.values())}, index=soil_type_mapping.keys())
 
-    # Convert soil_type and crop_type to numerical values using the mappings
-    soil_type_numerical = soil_type_mapping.get(soil_type, -1)  # -1 for unknown soil type
-    crop_type_numerical = crop_type_mapping.get(crop_type, -1)  # -1 for unknown crop type
+crop_types = df['Crop Type'].unique().tolist()
+crop_type_mapping = {crop: idx+1 for idx, crop in enumerate(sorted(crop_types))}
+Crop_Type = pd.DataFrame({'Encoded': list(crop_type_mapping.values())}, index=crop_type_mapping.keys())
 
-    # Prepare the input data for the model
-    input_data = [[temperature, humidity, moisture, soil_type_numerical, crop_type_numerical, nitrogen, phosphorous, potassium]]
+# Use the provided fertilizer mapping
+fertilizer_types = ["Urea", "DAP", "14-35-14", "28-28", "17-17-17", "20-20", "10-26-26"]
+Fertilizer = pd.DataFrame({'Encoded': [1, 2, 3, 4, 5, 6, 7]}, index=fertilizer_types)
+
+# Load the trained model
+def load_model():
+    try:
+        with open('Fertilizer_recommender.pkl', 'rb') as f:
+            model = pickle.load(f)
+        return model
+    except FileNotFoundError:
+        # For demonstration purposes
+        import joblib
+        return joblib.load('Fertilizer_recommender.pkl')
 
-    # Make the prediction
-    prediction = model.predict(input_data)
-    return prediction[0]
+model = load_model()
 
-# Step 5: Create Gradio interface
+def recommend_fertilizer(temperature, humidity, moisture, soil_type, crop_type, nitrogen, phosphorous, potassium):
+    """
+    Function to recommend fertilizer based on input parameters
+    """
+    # Get the encoded values for soil and crop types
+    soil_type_encoded = Soil_Type.loc[soil_type]['Encoded']
+    crop_type_encoded = Crop_Type.loc[crop_type]['Encoded']
+    
+    # Create input DataFrame with the exact column names used during training
+    # Note: The CSV shows column names with exact spelling (including typo in 'Temparature')
+    input_data = pd.DataFrame({
+        'Temparature': [temperature],  # Maintaining the typo as in the dataset
+        'Humidity ': [humidity],       # Note the space after Humidity
+        'Moisture': [moisture],
+        'Soil Type': [soil_type_encoded],
+        'Crop Type': [crop_type_encoded],
+        'Nitrogen': [nitrogen],
+        'Potassium': [potassium],      # Note: In the CSV, Potassium comes before Phosphorous
+        'Phosphorous': [phosphorous],
+    })
+    
+    # Make prediction
+    prediction = model.predict(input_data)[0]
+    
+    # Map prediction back to fertilizer name
+    try:
+        fertilizer_name = Fertilizer[Fertilizer['Encoded'] == prediction].index[0]
+        return fertilizer_name
+    except IndexError:
+        return f"Unknown fertilizer code: {prediction} - Please check your Fertilizer mapping."
+
+# Create Gradio interface with examples from the dataset
 iface = gr.Interface(
     fn=recommend_fertilizer,
     inputs=[
-        gr.Number(label="Temperature"),
-        gr.Number(label="Humidity"),
-        gr.Number(label="Moisture"),
-        gr.Textbox(label="Soil Type"),  # Use Textbox for soil type input
-        gr.Textbox(label="Crop Type"),   # Use Textbox for crop type input
-        gr.Number(label="Nitrogen"),
-        gr.Number(label="Phosphorous"),
-        gr.Number(label="Potassium"),
+        gr.Number(label="Temperature (°C)", value=26),
+        gr.Number(label="Humidity (%)", value=52),
+        gr.Number(label="Moisture (%)", value=38),
+        gr.Dropdown(choices=sorted(soil_types), label="Soil Type", value="Black"),
+        gr.Dropdown(choices=sorted(crop_types), label="Crop Type", value="Wheat"),
+        gr.Number(label="Nitrogen (%)", value=37),
+        gr.Number(label="Phosphorous (%)", value=0),
+        gr.Number(label="Potassium (%)", value=0),
     ],
     outputs=gr.Text(label="Recommended Fertilizer"),
     title="Fertilizer Recommender",
     description="Enter the environmental and crop details to get the best fertilizer recommendation.",
+    examples=[
+        # Example from your initial test case
+        [23, 40, 60, "Black", "Wheat", 30, 40, 50],
+        # Examples from the dataset
+        [26, 52, 38, "Sandy", "Maize", 37, 0, 0],  # Should recommend Urea
+        [29, 52, 45, "Loamy", "Sugarcane", 12, 36, 0],  # Should recommend DAP
+        [34, 65, 62, "Black", "Cotton", 7, 30, 9],  # Should recommend 14-35-14
+        [32, 62, 34, "Red", "Tobacco", 22, 20, 0]   # Should recommend 28-28
+    ],
     api_name="/api/predict_fertilizer"
 )
 
-# Step 6: Launch the app with show_error enabled
-iface.launch(show_error=True) # Added show_error=True to see detailed error messages
+# Launch the app
+iface.launch(show_error=True)