Upload app.py

app.py

@@ -1,121 +1,78 @@
-import os
-import uuid
-import joblib
-import json
-
-import gradio as gr
-import pandas as pd
-
-from huggingface_hub import CommitScheduler
-from pathlib import Path
-
-log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
-log_folder = log_file.parent
-
-scheduler = CommitScheduler(
-    repo_id="term-deposit-logs",
-    repo_type="dataset",
-    folder_path=log_folder,
-    path_in_repo="data",
-    every=2
-)
-
-term_deposit_predictor = joblib.load('model.joblib')
-
-age_input = gr.Number(label="Age")
-duration_input = gr.Number(label='Duration(Sec)')
-cc_contact_freq_input = gr.Number(label='CC Contact Freq')
-days_since_pc_input = gr.Number(label='Days Since PC')
-pc_contact_freq_input = gr.Number(label='Pc Contact Freq')
-job_input = gr.Dropdown(['admin.', 'blue-collar', 'technician', 'services', 'management',
-       'retired', 'entrepreneur', 'self-employed', 'housemaid', 'unemployed',
-       'student', 'unknown'],label="Job")
-marital_input = gr.Dropdown(['married', 'single', 'divorced', 'unknown'],label='Marital Status')
-education_input = gr.Dropdown(['experience', 'university degree', 'high school', 'professional.course',
-       'Others', 'illiterate'],label='Education')
-defaulter_input = gr.Dropdown(['no', 'unknown', 'yes'],label='Defaulter')
-home_loan_input = gr.Dropdown(['yes', 'no', 'unknown'],label='Home Loan')
-personal_loan_input = gr.Dropdown(['yes', 'no', 'unknown'],label='Personal Loan')
-communication_type_input = gr.Dropdown(['cellular', 'telephone'],label='Communication Type')
-last_contacted_input = gr.Dropdown(['may', 'jul', 'aug', 'jun', 'nov', 'apr', 'oct', 'mar', 'sep', 'dec'],label='Last Contacted')
-day_of_week_input = gr.Dropdown(['thu', 'mon', 'wed', 'tue', 'fri'],label='Day of Week')
-pc_outcome_input = gr.Dropdown(['nonexistent', 'failure', 'success'], label='PC Outcome')
-
-
-model_output = gr.Label(label="Subscribed")
-
-def predict_term_deposit(age, duration, cc_contact_freq, days_since_pc, pc_contact_freq, job, marital_status, education, 
-                         defaulter, home_loan, personal_loan, communication_type, last_contacted, 
-                         day_of_week, pc_outcome):
-    sample = {
-        'Age': age,
-        'Duration(Sec)': duration,
-        'CC Contact Freq': cc_contact_freq,
-        'Days Since PC': days_since_pc,
-        'PC Contact Freq': pc_contact_freq,
-        'Job': job,
-        'Marital Status': marital_status,
-        'Education': education,
-        'Defaulter': defaulter,
-        'Home Loan': home_loan,
-        'Personal Loan': personal_loan,
-        'Communication Type': communication_type,
-        'Last Contacted': last_contacted,
-        'Day of Week': day_of_week,
-        'PC Outcome': pc_outcome,
-    }
-    data_point = pd.DataFrame([sample])
-    prediction = term_deposit_predictor.predict(data_point).tolist()
-
-    with scheduler.lock:
-        with log_file.open("a") as f:
-            f.write(json.dumps(
-                {
-                    'Age': age,
-                    'Duration(Sec)': duration,
-                    'CC Contact Freq': cc_contact_freq,
-                    'Days Since PC': days_since_pc,
-                    'PC Contact Freq': pc_contact_freq,
-                    'Job': job,
-                    'Marital Status': marital_status,
-                    'Education': education,
-                    'Defaulter': defaulter,
-                    'Home Loan': home_loan,
-                    'Personal Loan': personal_loan,
-                    'Communication Type': communication_type,
-                    'Last Month Contacted': last_contacted,
-                    'Day of Week': day_of_week,
-                    'PC Outcome': pc_outcome,
-                    'prediction': prediction[0]
-                }
-            ))
-            f.write("\n")
-            
-    return prediction[0]
-
-demo = gr.Interface(
-    fn=predict_term_deposit,
-    inputs=[age_input,
-            duration_input,
-            cc_contact_freq_input,
-            days_since_pc_input,
-            pc_contact_freq_input,
-            job_input,
-            marital_input,
-            education_input,
-            defaulter_input,
-            home_loan_input,
-            personal_loan_input,
-            communication_type_input,
-            last_contacted_input,
-            day_of_week_input,
-            pc_outcome_input],
-    outputs=model_output,
-    title="Term Deposit Prediction",
-    description="This API allows you to predict the person who are going to likely subscribe the term deposit",
-    allow_flagging="auto",
-    concurrency_limit=8
-)
-
-demo.queue()
+import os
+import uuid
+import joblib
+import json
+
+import gradio as gr
+import pandas as pd
+
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+scheduler = CommitScheduler(
+    repo_id="machine-failure-logs",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+machine_failure_predictor = joblib.load('model.joblib')
+
+air_temperature_input = gr.Number(label='Air temperature [K]')
+process_temperature_input = gr.Number(label='Process temperature [K]')
+rotational_speed_input = gr.Number(label='Rotational speed [rpm]')
+torque_input = gr.Number(label='Torque [Nm]')
+tool_wear_input = gr.Number(label='Tool wear [min]')
+type_input = gr.Dropdown(
+    ['L', 'M', 'H'],
+    label='Type'
+)
+
+model_output = gr.Label(label="Machine failure")
+
+def predict_machine_failure(air_temperature, process_temperature, rotational_speed, torque, tool_wear, type):
+    sample = {
+        'Air temperature [K]': air_temperature,
+        'Process temperature [K]': process_temperature,
+        'Rotational speed [rpm]': rotational_speed,
+        'Torque [Nm]': torque,
+        'Tool wear [min]': tool_wear,
+        'Type': type
+    }
+    data_point = pd.DataFrame([sample])
+    prediction = machine_failure_predictor.predict(data_point).tolist()
+
+    with scheduler.lock:
+        with log_file.open("a") as f:
+            f.write(json.dumps(
+                {
+                    'Air temperature [K]': air_temperature,
+                    'Process temperature [K]': process_temperature,
+                    'Rotational speed [rpm]': rotational_speed,
+                    'Torque [Nm]': torque,
+                    'Tool wear [min]': tool_wear,
+                    'Type': type,
+                    'prediction': prediction[0]
+                }
+            ))
+            f.write("\n")
+            
+    return prediction[0]
+
+demo = gr.Interface(
+    fn=predict_machine_failure,
+    inputs=[air_temperature_input, process_temperature_input, rotational_speed_input, 
+            torque_input, tool_wear_input, type_input],
+    outputs=model_output,
+    title="Machine Failure Predictor",
+    description="This API allows you to predict the machine failure status of an equipment",
+    allow_flagging="auto",
+    concurrency_limit=8
+)
+
+demo.queue()
 demo.launch(share=False)