Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+
+
+data = pd.read_excel('crop.xlsx')
+data['CROP'].unique()
+sorted(data['CROP'].unique())
+le=LabelEncoder()
+data['CROP_EN']=le.fit_transform(data['CROP'])
+x=data.iloc[:,:-2]
+y=data['CROP_EN']
+x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)
+model=KNeighborsClassifier(n_neighbors=3)
+model.fit(x_train,y_train)
+y_pred=model.predict(x_test)
+res = [
+    'apple', 'banana', 'blackgram', 'chickpea', 'coconut', 'coffee', 'cotton',
+    'grapes', 'jute', 'kidneybeans', 'lentil', 'maize', 'mango', 'mothbeans',
+    'mungbean', 'muskmelon', 'orange', 'papaya', 'pigeonpeas', 'pomegranate',
+    'rice', 'watermelon'
+]
+def predict(n,p,k,temp,hum,ph,rain):
+    pred = model.predict([[n,p,k,temp,hum,ph,rain]])
+    return res[pred[0]]
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Nitrogen"),
+        gr.Number(label="PHOSPHORUS"),
+        gr.Number(label="Potassium"),
+        gr.Number(label="Temperature"),
+        gr.Number(label="Humidity"),
+        gr.Number(label="Soil PH"),
+        gr.Number(label="Rainfall"),
+    ],
+    outputs=[
+        gr.Textbox(label="Predicted Crop"),
+    ],
+    title="Crop Prediction Using machine Learning",
+    # description="This interface takes some input and generates 7 numerical outputs along with one text output."
+)
+
+
+interface.launch()
+