Spaces:
Build error
Build error
File size: 3,962 Bytes
7d39c70 a0c4a5e 7d39c70 a0c4a5e 7d39c70 a0c4a5e 7d39c70 a0c4a5e 7d39c70 a0c4a5e 7d39c70 a0c4a5e 7d39c70 a0c4a5e 7d39c70 a0c4a5e 7d39c70 a0c4a5e 7d39c70 a0c4a5e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
import gradio as gr
import pandas as pd
import pickle
import numpy as np
# Extract unique values from the dataset
df = pd.read_csv('Fertilizer Prediction.csv')
# 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())
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())
# 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')
model = load_model()
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 (°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"
)
# Launch the app
iface.launch(show_error=True) |