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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() |