Update app.py

app.py

@@ -1,130 +1,138 @@
-import numpy as np
-import pandas as pd
-import gradio as gr
-import joblib
-import tensorflow as tf
-
-# Load artifacts
-model = tf.keras.models.load_model('model/my_model_low_build.keras')
-scaler = joblib.load('model/scaler_minor_low_build.pkl')
-soil_encoder = joblib.load('model/soil_encoder_low_build.pkl')
-crop_encoder = joblib.load('model/crop_encoder_low_build.pkl')
-fertilizer_encoder = joblib.load('model/fertilizer_encoder_low_build.pkl')
-
-# Crop requirements (same as before)
-crop_requirements = {
-    'cotton':    {'Nitrogen': (80, 120), 'Phosphorus': (40, 60),  'Potassium': (80, 120)},
-    'orange':    {'Nitrogen': (120, 150), 'Phosphorus': (50, 70),  'Potassium': (150, 200)},
-    'wheat':     {'Nitrogen': (100, 150), 'Phosphorus': (40, 60),  'Potassium': (40, 60)},
-    'maize':     {'Nitrogen': (150, 200), 'Phosphorus': (50, 80),  'Potassium': (50, 80)},
-    'rice':      {'Nitrogen': (80, 120),  'Phosphorus': (40, 60),  'Potassium': (60, 80)},
-    'potato':    {'Nitrogen': (150, 200), 'Phosphorus': (60, 80),  'Potassium': (150, 200)},
-    'tomato':    {'Nitrogen': (100, 150), 'Phosphorus': (50, 70),  'Potassium': (150, 200)},
-    'carrot':    {'Nitrogen': (60, 100),  'Phosphorus': (30, 50),  'Potassium': (60, 80)},
-    'cabbage':   {'Nitrogen': (150, 200), 'Phosphorus': (70, 100), 'Potassium': (150, 200)},
-    'banana':    {'Nitrogen': (150, 200), 'Phosphorus': (70, 100), 'Potassium': (200, 250)},
-    'onion':     {'Nitrogen': (80, 120),  'Phosphorus': (40, 60),  'Potassium': (80, 100)},
-    'pepper':    {'Nitrogen': (100, 150), 'Phosphorus': (50, 70),  'Potassium': (150, 200)},
-    'lettuce':   {'Nitrogen': (60, 100),  'Phosphorus': (30, 50),  'Potassium': (40, 60)},
-    'sunflower': {'Nitrogen': (80, 120),  'Phosphorus': (40, 60),  'Potassium': (60, 80)},
-    'soybean':   {'Nitrogen': (0, 20),    'Phosphorus': (40, 60),  'Potassium': (40, 60)},  # Legume: fixes its own N
-    'tobacco':   {'Nitrogen': (120, 150), 'Phosphorus': (40, 60),  'Potassium': (60, 80)},
-    'sugarcane': {'Nitrogen': (100, 150), 'Phosphorus': (40, 60),  'Potassium': (150, 200)},
-    'peanut':    {'Nitrogen': (0, 20),    'Phosphorus': (40, 60),  'Potassium': (60, 80)},  # Legume: fixes N
-    'coffee':    {'Nitrogen': (80, 120),  'Phosphorus': (40, 60),  'Potassium': (80, 120)},
-    'tea':       {'Nitrogen': (100, 150), 'Phosphorus': (50, 70),  'Potassium': (100, 150)}
-}
-
-
-def crop_specific_recommendation(raw_input):
-    recommendations = []
-    crop = raw_input[5].lower()
-
-    if crop in crop_requirements:
-        ideal = crop_requirements[crop]
-        n = raw_input[2]
-        p = raw_input[3]
-        k = raw_input[4]
-
-        # Nitrogen check
-        if n < ideal['Nitrogen'][0]:
-            recommendations.append(f"Nitrogen is low for {crop} (current: {n}, ideal: {ideal['Nitrogen'][0]}-{ideal['Nitrogen'][1]})")
-        elif n > ideal['Nitrogen'][1]:
-            recommendations.append(f"Nitrogen is high for {crop} (current: {n}, ideal: {ideal['Nitrogen'][0]}-{ideal['Nitrogen'][1]})")
-
-        # Phosphorus check
-        if p < ideal['Phosphorus'][0]:
-            recommendations.append(f"Phosphorus is low for {crop} (current: {p}, ideal: {ideal['Phosphorus'][0]}-{ideal['Phosphorus'][1]})")
-        elif p > ideal['Phosphorus'][1]:
-            recommendations.append(f"Phosphorus is high for {crop} (current: {p}, ideal: {ideal['Phosphorus'][0]}-{ideal['Phosphorus'][1]})")
-
-        # Potassium check
-        if k < ideal['Potassium'][0]:
-            recommendations.append(f"Potassium is low for {crop} (current: {k}, ideal: {ideal['Potassium'][0]}-{ideal['Potassium'][1]})")
-        elif k > ideal['Potassium'][1]:
-            recommendations.append(f"Potassium is high for {crop} (current: {k}, ideal: {ideal['Potassium'][0]}-{ideal['Potassium'][1]})")
-    else:
-        recommendations.append(f"No specific recommendations available for {crop}")
-
-    return "\n".join(recommendations) if recommendations else f"All nutrient levels are optimal for {crop}"
-
-
-def predict_fertilizer(temperature, soil, nitrogen, phosphorus, potassium, crop):
-    try:
-        # Validate inputs
-        crop = crop.lower()
-        if soil not in soil_encoder.classes_:
-            raise ValueError(f"Invalid soil type. Choose from {list(soil_encoder.classes_)}")
-        if crop not in crop_encoder.classes_:
-            raise ValueError(f"Invalid crop type. Choose from {list(crop_encoder.classes_)}")
-
-        # Prepare input
-        num_features = np.array([[temperature, nitrogen, phosphorus, potassium]])
-        scaled_num = scaler.transform(num_features)
-        encoded_soil = soil_encoder.transform([soil])[0]
-        encoded_crop = crop_encoder.transform([crop])[0]
-
-        model_input = np.array([[
-            scaled_num[0][0],    # Temperature
-            encoded_soil,        # Soil
-            scaled_num[0][1],    # Nitrogen
-            scaled_num[0][2],    # Phosphorus
-            scaled_num[0][3],    # Potassium
-            encoded_crop         # Crop
-        ]])
-
-        # Predict
-        pred = model.predict(model_input)
-        fertilizer = fertilizer_encoder.inverse_transform([np.argmax(pred)])[0]
-
-        # Generate recommendations
-        raw_input = [temperature, soil, nitrogen, phosphorus, potassium, crop]
-        crop_advice = crop_specific_recommendation(raw_input)
-
-        return fertilizer, crop_advice
-
-    except Exception as e:
-        return str(e), ""
-
-# Create Gradio interface
-inputs = [
-    gr.Number(label="Temperature (°C)"),
-    gr.Dropdown(label="Soil Type", choices=list(soil_encoder.classes_)),
-    gr.Number(label="Nitrogen Level (kg/ha)"),
-    gr.Number(label="Phosphorus Level (kg/ha)"),
-    gr.Number(label="Potassium Level (kg/ha)"),
-    gr.Dropdown(label="Crop Type", choices=list(crop_encoder.classes_))
-]
-
-outputs = [
-    gr.Textbox(label="Recommended Fertilizer"),
-    gr.Textbox(label="Crop Advice")
-]
-
-gr.Interface(
-    fn=predict_fertilizer,
-    inputs=inputs,
-    outputs=outputs,
-    title="Fertilizer Recommendation System",
-    description="Predict optimal fertilizer based on soil and crop conditions"
+import numpy as np
+import pandas as pd
+import gradio as gr
+import joblib
+import tensorflow as tf
+
+# Load artifacts
+# model = tf.keras.models.load_model('model/my_model_low_build.keras')
+# scaler = joblib.load('model/scaler_minor_low_build.pkl')
+# soil_encoder = joblib.load('model/soil_encoder_low_build.pkl')
+# crop_encoder = joblib.load('model/crop_encoder_low_build.pkl')
+# fertilizer_encoder = joblib.load('model/fertilizer_encoder_low_build.pkl')
+
+model = tf.keras.models.load_model('model/my_model.keras')
+scaler = joblib.load('model/scaler.pkl')
+soil_encoder = joblib.load('model/soil_encoder.pkl')
+crop_encoder = joblib.load('model/crop_encoder.pkl')
+fertilizer_encoder = joblib.load('model/fertilizer_encoder.pkl')
+
+
+
+# Crop requirements (same as before)
+crop_requirements = {
+    'cotton':    {'Nitrogen': (80, 120), 'Phosphorus': (40, 60),  'Potassium': (80, 120)},
+    'orange':    {'Nitrogen': (120, 150), 'Phosphorus': (50, 70),  'Potassium': (150, 200)},
+    'wheat':     {'Nitrogen': (100, 150), 'Phosphorus': (40, 60),  'Potassium': (40, 60)},
+    'maize':     {'Nitrogen': (150, 200), 'Phosphorus': (50, 80),  'Potassium': (50, 80)},
+    'rice':      {'Nitrogen': (80, 120),  'Phosphorus': (40, 60),  'Potassium': (60, 80)},
+    'potato':    {'Nitrogen': (150, 200), 'Phosphorus': (60, 80),  'Potassium': (150, 200)},
+    'tomato':    {'Nitrogen': (100, 150), 'Phosphorus': (50, 70),  'Potassium': (150, 200)},
+    'carrot':    {'Nitrogen': (60, 100),  'Phosphorus': (30, 50),  'Potassium': (60, 80)},
+    'cabbage':   {'Nitrogen': (150, 200), 'Phosphorus': (70, 100), 'Potassium': (150, 200)},
+    'banana':    {'Nitrogen': (150, 200), 'Phosphorus': (70, 100), 'Potassium': (200, 250)},
+    'onion':     {'Nitrogen': (80, 120),  'Phosphorus': (40, 60),  'Potassium': (80, 100)},
+    'pepper':    {'Nitrogen': (100, 150), 'Phosphorus': (50, 70),  'Potassium': (150, 200)},
+    'lettuce':   {'Nitrogen': (60, 100),  'Phosphorus': (30, 50),  'Potassium': (40, 60)},
+    'sunflower': {'Nitrogen': (80, 120),  'Phosphorus': (40, 60),  'Potassium': (60, 80)},
+    'soybean':   {'Nitrogen': (0, 20),    'Phosphorus': (40, 60),  'Potassium': (40, 60)},  # Legume: fixes its own N
+    'tobacco':   {'Nitrogen': (120, 150), 'Phosphorus': (40, 60),  'Potassium': (60, 80)},
+    'sugarcane': {'Nitrogen': (100, 150), 'Phosphorus': (40, 60),  'Potassium': (150, 200)},
+    'peanut':    {'Nitrogen': (0, 20),    'Phosphorus': (40, 60),  'Potassium': (60, 80)},  # Legume: fixes N
+    'coffee':    {'Nitrogen': (80, 120),  'Phosphorus': (40, 60),  'Potassium': (80, 120)},
+    'tea':       {'Nitrogen': (100, 150), 'Phosphorus': (50, 70),  'Potassium': (100, 150)}
+}
+
+
+def crop_specific_recommendation(raw_input):
+    recommendations = []
+    crop = raw_input[5].lower()
+
+    if crop in crop_requirements:
+        ideal = crop_requirements[crop]
+        n = raw_input[2]
+        p = raw_input[3]
+        k = raw_input[4]
+
+        # Nitrogen check
+        if n < ideal['Nitrogen'][0]:
+            recommendations.append(f"Nitrogen is low for {crop} (current: {n}, ideal: {ideal['Nitrogen'][0]}-{ideal['Nitrogen'][1]})")
+        elif n > ideal['Nitrogen'][1]:
+            recommendations.append(f"Nitrogen is high for {crop} (current: {n}, ideal: {ideal['Nitrogen'][0]}-{ideal['Nitrogen'][1]})")
+
+        # Phosphorus check
+        if p < ideal['Phosphorus'][0]:
+            recommendations.append(f"Phosphorus is low for {crop} (current: {p}, ideal: {ideal['Phosphorus'][0]}-{ideal['Phosphorus'][1]})")
+        elif p > ideal['Phosphorus'][1]:
+            recommendations.append(f"Phosphorus is high for {crop} (current: {p}, ideal: {ideal['Phosphorus'][0]}-{ideal['Phosphorus'][1]})")
+
+        # Potassium check
+        if k < ideal['Potassium'][0]:
+            recommendations.append(f"Potassium is low for {crop} (current: {k}, ideal: {ideal['Potassium'][0]}-{ideal['Potassium'][1]})")
+        elif k > ideal['Potassium'][1]:
+            recommendations.append(f"Potassium is high for {crop} (current: {k}, ideal: {ideal['Potassium'][0]}-{ideal['Potassium'][1]})")
+    else:
+        recommendations.append(f"No specific recommendations available for {crop}")
+
+    return "\n".join(recommendations) if recommendations else f"All nutrient levels are optimal for {crop}"
+
+
+def predict_fertilizer(temperature, soil, nitrogen, phosphorus, potassium, crop):
+    try:
+        # Validate inputs
+        crop = crop.lower()
+        if soil not in soil_encoder.classes_:
+            raise ValueError(f"Invalid soil type. Choose from {list(soil_encoder.classes_)}")
+        if crop not in crop_encoder.classes_:
+            raise ValueError(f"Invalid crop type. Choose from {list(crop_encoder.classes_)}")
+
+        # Prepare input
+        num_features = np.array([[temperature, nitrogen, phosphorus, potassium]])
+        scaled_num = scaler.transform(num_features)
+        encoded_soil = soil_encoder.transform([soil])[0]
+        encoded_crop = crop_encoder.transform([crop])[0]
+
+        model_input = np.array([[
+            scaled_num[0][0],    # Temperature
+            encoded_soil,        # Soil
+            scaled_num[0][1],    # Nitrogen
+            scaled_num[0][2],    # Phosphorus
+            scaled_num[0][3],    # Potassium
+            encoded_crop         # Crop
+        ]])
+
+        # Predict
+        pred = model.predict(model_input)
+        fertilizer = fertilizer_encoder.inverse_transform([np.argmax(pred)])[0]
+
+        # Generate recommendations
+        raw_input = [temperature, soil, nitrogen, phosphorus, potassium, crop]
+        crop_advice = crop_specific_recommendation(raw_input)
+
+        return fertilizer, crop_advice
+
+    except Exception as e:
+        return str(e), ""
+
+# Create Gradio interface
+inputs = [
+    gr.Number(label="Temperature (°C)"),
+    gr.Dropdown(label="Soil Type", choices=list(soil_encoder.classes_)),
+    gr.Number(label="Nitrogen Level (kg/ha)"),
+    gr.Number(label="Phosphorus Level (kg/ha)"),
+    gr.Number(label="Potassium Level (kg/ha)"),
+    gr.Dropdown(label="Crop Type", choices=list(crop_encoder.classes_))
+]
+
+outputs = [
+    gr.Textbox(label="Recommended Fertilizer"),
+    gr.Textbox(label="Crop Advice")
+]
+
+gr.Interface(
+    fn=predict_fertilizer,
+    inputs=inputs,
+    outputs=outputs,
+    title="Fertilizer Recommendation System",
+    description="Predict optimal fertilizer based on soil and crop conditions"
 ).launch()