awd

app.py

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+import gradio as gr
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+
+# 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'])
+
+# 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
+}
+
+# application rates
+application_rates = {
+    'P': 30,
+    'K': 50,
+    'Ca': 40,
+    'Mg': 20,
+    'S': 25,
+    'Zn': 5,
+    'B': 2,
+    'Fe': 10,
+    'Cu': 1,
+    'Mn': 4
+}
+
+# soil density and depth
+soil_density = 1800
+soil_depth = 0.2
+
+# calculate amounts
+def get_fertilizer_recommendation(row, land_size_m2, fallow_years):
+    deficiencies = []
+    fertilizer_amounts = {}
+    for nutrient, threshold in thresholds.items():
+        if row[nutrient] < threshold:
+            nutrient_name = nutrient.split('_')[-1]
+            deficiencies.append(nutrient_name)
+            base_amount_per_m2 = application_rates[nutrient_name] / 10000
+            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
+    else:
+        return 'No deficiency', {}
+
+# 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."
+)
+
+if __name__ == "__main__":
+    demo.launch()