ok

app.py

@@ -1,51 +1,60 @@
-import gradio as gr
 import pandas as pd
 from sklearn.preprocessing import LabelEncoder
+import gradio as gr
 
 # Load data
-data = pd.read_csv('chittor_final1.csv')
-
-# Encoding
-encode_soil = LabelEncoder()
-data['Soil_Type'] = encode_soil.fit_transform(data['Soil_type'])
-
-encode_crop = LabelEncoder()
-data['Crop_Type'] = encode_crop.fit_transform(data['Crop_type'])
+def load_data(file_path):
+    try:
+        data = pd.read_csv(file_path)
+        return data
+    except Exception as e:
+        print(f"Error loading data: {e}")
+        return None
 
-# nutrient thresholds
-thresholds = {
-    'Avail_P': 10,
-    'Exch_K': 50,
-    'Avail_Ca': 200,
-    'Avail_Mg': 50,
-    'Avail_S': 10,
-    'Avail_Zn': 1,
-    'Avail_B': 0.5,
-    'Avail_Fe': 4,
-    'Avail_Cu': 0.3,
-    'Avail_Mn': 5
-}
+# Encode soil and crop types
+def encode_soil_crop(data):
+    encode_soil = LabelEncoder()
+    data['Soil_Type'] = encode_soil.fit_transform(data['Soil_type'])
+    encode_crop = LabelEncoder()
+    data['Crop_Type'] = encode_crop.fit_transform(data['Crop_type'])
+    return data
 
-# application rates
-application_rates = {
-    'P': 30,
-    'K': 50,
-    'Ca': 40,
-    'Mg': 20,
-    'S': 25,
-    'Zn': 5,
-    'B': 2,
-    'Fe': 10,
-    'Cu': 1,
-    'Mn': 4
-}
+# Define nutrient thresholds and application rates
+def define_thresholds_application_rates():
+    thresholds = {
+        'Avail_P': 10,
+        'Exch_K': 50,
+        'Avail_Ca': 200,
+        'Avail_Mg': 50,
+        'Avail_S': 10,
+        'Avail_Zn': 1,
+        'Avail_B': 0.5,
+        'Avail_Fe': 4,
+        'Avail_Cu': 0.3,
+        'Avail_N': 5
+    }
+    application_rates = {
+        'P': 30,
+        'K': 50,
+        'Ca': 40,
+        'Mg': 20,
+        'S': 25,
+        'Zn': 5,
+        'B': 2,
+        'Fe': 10,
+        'Cu': 1,
+        'N': 4
+    }
+    return thresholds, application_rates
 
-# soil density and depth
-soil_density = 1800
-soil_depth = 0.2
+# Define soil density and depth
+def define_soil_density_depth():
+    soil_density = 1800
+    soil_depth = 0.2
+    return soil_density, soil_depth
 
-# calculate amounts
-def get_fertilizer_recommendation(row, land_size_m2, fallow_years):
+# Function to get fertilizer recommendation
+def get_fertilizer_recommendation(row, land_size_m2, fallow_years, thresholds, application_rates):
     deficiencies = []
     fertilizer_amounts = {}
     for nutrient, threshold in thresholds.items():
@@ -56,30 +65,42 @@ def get_fertilizer_recommendation(row, land_size_m2, fallow_years):
             total_amount = base_amount_per_m2 * land_size_m2 * (1 + 0.1 * fallow_years)
             fertilizer_amounts[nutrient_name] = round(total_amount, 2)
     if deficiencies:
-        return 'Fertilizer needed for'+ ', '.join(deficiencies), fertilizer_amounts
+        return 'Fertilizer needed for'', '.join(deficiencies), fertilizer_amounts
     else:
-        return 'No deficiency', {}
+        return 'No deficiency, Manure Recommended', {}
+
+# Gradio application
+def gradio_application():
+    file_path = 'chittor_final1.csv'
+    data = load_data(file_path)
+    if data is not None:
+        data = encode_soil_crop(data)
+        thresholds, application_rates = define_thresholds_application_rates()
+        soil_density, soil_depth = define_soil_density_depth()
+
+        def fertilizer_recommendation(soil_type_input, crop_type_input, land_size_m2, fallow_years):
+            filtered_data = data[(data['Soil_type'] == soil_type_input) & (data['Crop_type'] == crop_type_input)]
+            if filtered_data.empty:
+                return "No data available for the given soil type and crop type."
+            else:
+                row = filtered_data.iloc[0]
+                recommendation, amounts = get_fertilizer_recommendation(row, land_size_m2, fallow_years, thresholds, application_rates)
+                return f"Recommendation: {recommendation}\nFertilizer Amounts (in kg):\n" + "\n".join(f"{nutrient}: {amount} kg" for nutrient, amount in amounts.items())
+
+        demo = gr.Interface(
+            fn=fertilizer_recommendation,
+            inputs=[
+                gr.Dropdown(label="Soil Type", choices=list(data['Soil_type'].unique())),
+                gr.Dropdown(label="Crop Type", choices=list(data['Crop_type'].unique())),
+                gr.Number(label="Land Size (m2)"),
+                gr.Number(label="Fallow Years")
+            ],
+            outputs="text",
+            title="Fertilizer Recommendation App",
+            description="Enter the soil type, crop type, land size, and fallow years to get a fertilizer recommendation."
+        )
 
-# Gradio app
-demo = gr.Interface(
-    fn=lambda soil_type, crop_type, land_size_m2, fallow_years: get_fertilizer_recommendation(
-        data[(data['Soil_Type'] == encode_soil.transform([soil_type])[0]) & (data['Crop_Type'] == encode_crop.transform([crop_type])[0])].iloc[0],
-        land_size_m2,
-        fallow_years
-    ),
-    inputs=[
-        gr.Dropdown(list(data['Soil_type'].unique()), label="Soil Type"), # Convert the numpy array to a list
-        gr.Dropdown(list(data['Crop_type'].unique()), label="Crop Type"), # Convert the numpy array to a list
-        gr.Number(label="Land Size (m²)"),
-        gr.Number(label="Fallow Years")
-    ],
-    outputs=[
-        gr.Textbox(label="Recommendation"),
-        gr.JSON(label="Fertilizer Amounts (in kg)")
-    ],
-    title="Fertilizer Recommendation App",
-    description="Get fertilizer recommendations based on soil type, crop type, land size, and fallow years."
-)
+        demo.launch()
 
 if __name__ == "__main__":
-    demo.launch()
+    gradio_application()