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import gradio as gr |
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import pandas as pd |
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import lightgbm as lgb |
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import numpy as np |
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from sklearn.model_selection import train_test_split |
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from sklearn.preprocessing import LabelEncoder |
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import os |
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import torch |
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from torchvision import models, transforms |
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from PIL import Image |
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url = "https://raw.githubusercontent.com/Pushpinder-Singh06/CSV-Files/refs/heads/main/crop_cleaned%20data.csv " |
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data = pd.read_csv(url) |
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X = data.drop('label', axis=1) |
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y = data['label'] |
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le = LabelEncoder() |
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y_encoded = le.fit_transform(y) |
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X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.3, random_state=0) |
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model = lgb.LGBMClassifier() |
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model.fit(X_train, y_train) |
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def predict_crop(nitrogen, phosphorus, potassium, temperature, humidity, soil_pH, rainfall): |
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input_data = np.array([[nitrogen, phosphorus, potassium, temperature, humidity, soil_pH, rainfall]]) |
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pred = model.predict(input_data)[0] |
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crop_name = le.inverse_transform([pred])[0] |
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image_path = f"crop_images/{crop_name}.jpeg" |
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if not os.path.exists(image_path): |
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image_path = None |
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return image_path, f"🌾 Recommended crop for your field: *{crop_name}*" |
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with gr.Blocks() as demo: |
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gr.Markdown("# 🌾 **Which Crop Should I Grow?**") |
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with gr.Tabs(): |
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with gr.Row(): |
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nitrogen = gr.Slider(0, 140, step=1, label="Nitrogen (kg/ha)") |
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phosphorus = gr.Slider(5, 95, step=1, label="Phosphorus (kg/ha)") |
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potassium = gr.Slider(5, 82, step=1, label="Potassium (kg/ha)") |
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with gr.Row(): |
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temperature = gr.Slider(15.63, 36.32, step=0.1, label="Temperature (°C)") |
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humidity = gr.Slider(14.2, 99.98, step=1, label="Humidity (%)") |
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with gr.Row(): |
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soil_pH = gr.Slider(0, 14, step=0.1, label="Soil pH") |
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rainfall = gr.Slider(20.21, 253.72, step=1, label="Rainfall (mm)") |
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predict_btn = gr.Button("Predict Crop") |
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crop_image_output = gr.Image(label="🌿 Crop Image") |
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crop_text_output = gr.Markdown() |
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predict_btn.click(fn=predict_crop, |
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inputs=[nitrogen, phosphorus, potassium, temperature, humidity, soil_pH, rainfall], |
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outputs=[crop_image_output, crop_text_output]) |
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demo.launch() |
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