Update app.py

app.py

@@ -1,29 +1,31 @@
+import pandas as pd
 import gradio as gr
-import pickle
-import pickletools
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
 
 
-def make_prediction(MEAN, VARIANCE, KURTOSIS):
-    with open("decision.pkl", "rb") as f:
-        rot = pickle.load(f)
-        preds = rot.predict([[MEAN, VARIANCE, KURTOSIS]])
-        print(preds)
-        return preds
+def fault_predictor(mean, variance, kurtosis):
+    df = pd.read_csv('final_feat_xtract.csv')
+    X = df[['Mean', 'Variance', 'Kurtosis']]
+    y = df['Condition']
+    X_train, _, y_train, _ = train_test_split(
+        X, y, test_size=0.2, random_state=42)
 
+    model = DecisionTreeClassifier()
+    model.fit(X_train, y_train)
 
-MEAN_input = gr.Number(label="Enter the mean value:")
-VARIANCE_input = gr.Number(label="Enter the variance value:")
-KURTOSIS_input = gr.Number(label="Enter the kurtosis value:")
-output = gr.Textbox(label="CONDITION OF THE MACHINE")
+    user_input_df = pd.DataFrame(
+        {'Mean': [mean], 'Variance': [variance], 'Kurtosis': [kurtosis]})
+    prediction = model.predict(user_input_df)
+    return prediction[0]
 
-app = gr.Interface(fn=make_prediction, inputs=[
-                   MEAN_input, VARIANCE_input, KURTOSIS_input], outputs=output, title="MOTOR FAULT DETECTION",
-                   description="This is an END to END EMBEDDED DIGITAL SIGNAL PROCESSING(EDSP) project done to predict the condition of the motor by giving the inputs in the prompt. \n\n"
-                   "This fault detection project has been deployed to showcase the main objective of the condition of the machine wheather it is in a healthy or in an unhealthy condition. \n\n"
-                   # "MODEL DEVELOPMENT AND MODEL DEPLOYMENT: PRANAVADHAR A. \n\n"
-                   "DEPLOYMENT TOOL: GRADIO \n\n"
-                   # "GITHUB: https://github.com/Pranavadhar \n\n"
-                   # "LINKED IN: https://www.linkedin.com/in/pranavadhar-a-b19525289/ \n\n")
-                   )
 
-app.launch(share=True)
+iface = gr.Interface(fn=fault_predictor,
+                     inputs=["number", "number", "number"],
+                     outputs=gr.Textbox(label="Condition of the Machine"),
+                     live=True,
+                     title="MACHINE CONDITION DETECTION - AN EDSP END SEM PROJECT",
+                     description="This is an END to END EMBEDDED DIGITAL SIGNAL PROCESSING(EDSP) project done to predict the condition of the motor by giving the inputs in the prompt. \n\n"
+                     "This fault detection project has been deployed to showcase the main objective of the condition of the machine whether it is in a healthy or in an unhealthy condition. \n\n"
+                     "DEPLOYMENT TOOL: GRADIO \n\n")
+iface.launch(share=True)