Create app.py

app.py

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+import numpy as np
+import pandas as pd
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+import gradio as gr
+def rainPrediction(fileCSVName):
+    #Importing necessary libraries
+    #Storing the values from the dataset in a variable
+    if fileCSVName == "weatherAUS.csv":
+      dataset = pd.read_csv("/weatherAUS.csv")
+    #D
+    X = dataset.iloc[:,[1,2,3,4,7,8,9,10,11,12,13,14,15,16,18,19,20,21]].values
+    Y = dataset.iloc[:,-1].values
+
+    #Reshaping Y from a 1-dimensional(a[n]) array into a 2-dimensional(a[n][m]) array 
+    Y = Y.reshape(-1,1)
+
+    #Removing NA from the dataset and replacing it with the most frequent value in that column
+    
+    imputer = SimpleImputer(missing_values=np.nan,strategy='most_frequent')
+    X = imputer.fit_transform(X)
+    Y = imputer.fit_transform(Y)
+
+    #Encoding non-numerical(i.e: W,WNW) values into numerical values(i.e: 1,2,3,4)
+
+    le1 = LabelEncoder()
+    X[:,0] = le1.fit_transform(X[:,0])
+    le2 = LabelEncoder()
+    X[:,4] = le2.fit_transform(X[:,4])
+    le3 = LabelEncoder()
+    X[:,6] = le3.fit_transform(X[:,6])
+    le4 = LabelEncoder()
+    X[:,7] = le4.fit_transform(X[:,7])
+    le5 = LabelEncoder()
+    X[:,-1] = le5.fit_transform(X[:,-1])
+    le6 = LabelEncoder()
+    Y = le6.fit_transform(Y)
+
+    #Feature scaling to minimize data scattering
+    
+    sc = StandardScaler()
+    X = sc.fit_transform(X)
+
+    #Dividing the dataset into 2 parts namely training data and testing data
+
+    X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2,random_state=0)
+
+    #Training our model
+
+    classifier = RandomForestClassifier(n_estimators=100,random_state=0)
+    classifier.fit(X_train,Y_train)
+    classifier.score(X_train,Y_train)
+    Y_test = Y_test.reshape(-1,1)
+    Y_pred = classifier.predict(X_test)
+    Y_pred = le6.inverse_transform(Y_pred)
+    Y_test = le6.inverse_transform(Y_test)
+    Y_test = Y_test.reshape(-1,1)
+    Y_pred = Y_pred.reshape(-1,1)
+
+    #Concatenating our test and prediction result into a dataset
+    df = np.concatenate((Y_test,Y_pred),axis=1)
+    dataframe = pd.DataFrame(df,columns=['Rain Tomorrow','Rain Prediction'])
+
+    #Checking the accuracy
+
+    print(accuracy_score(Y_test,Y_pred))
+
+    #Print .csv file
+    #answer = dataframe.to_csv("predictions.csv")
+
+    # return pd.read_csv("predictions.csv")
+    return dataframe
+  
+app = gr.Interface(rainPrediction, "text", gr.outputs.Dataframe(headers=["Rain Tomorrow", "Rain Prediction"],label="All data"))
+app.launch(debug=True)