Update app.py

app.py

@@ -1,101 +1,101 @@
-import gradio as gr
-import pandas as pd
-import joblib
-import os
-
-# Load the models
-preprocessor_path = os.path.join('.', 'preprocessor.joblib')
-model_path = os.path.join('.', 'Best_model.joblib')
-
-preprocessor = joblib.load('C:\Users\USER\Documents\DS\TMP\preprocessor.joblib')
-best_model = joblib.load('C:\Users\USER\Documents\DS\TMP\Best_model.joblib')
-
-# Define prediction function
-def predict(age, job, marital, education, default, housing, loan, contact, month, day_of_week, duration, campaign, pdays, previous, poutcome, emp_var_rate, cons_price_idx, cons_conf_idx, euribor3m, nr_employed):
-    # Create a DataFrame for the input data
-    data = pd.DataFrame({
-        'age': [age],
-        'job': [job],
-        'marital': [marital],
-        'education': [education],
-        'default': [default],
-        'housing': [housing],
-        'loan': [loan],
-        'contact': [contact],
-        'month': [month],
-        'day_of_week': [day_of_week],
-        'duration': [duration],
-        'campaign': [campaign],
-        'pdays': [pdays],
-        'previous': [previous],
-        'poutcome': [poutcome],
-        'emp.var.rate': [emp_var_rate],
-        'cons.price.idx': [cons_price_idx],
-        'cons.conf.idx': [cons_conf_idx],
-        'euribor3m': [euribor3m],
-        'nr.employed': [nr_employed]
-    })
-
-    # Preprocess the data
-    preprocessed_data = preprocessor.transform(data)
-
-    # Make predictions
-    prediction = best_model.predict(preprocessed_data)
-    probability = best_model.predict_proba(preprocessed_data)
-
-    return {
-        "Prediction": prediction[0],
-        "Probability (Yes)": probability[0][1],
-        "Probability (No)": probability[0][0]
-    }
-
-# Define the interface
-def gradio_interface():
-    with gr.Blocks() as app:
-        gr.Markdown("# Bank Marketing Campaign Prediction")
-
-        with gr.Row():
-            age = gr.Number(label="Age", value=30)
-            job = gr.Dropdown(["housemaid", "services", "admin.", "blue-collar", "technician", "retired", "management", "unemployed", "self-employed", "unknown", "entrepreneur", "student"], label="Job")
-            marital = gr.Dropdown(["married", "single", "divorced", "unknown"], label="Marital Status")
-            education = gr.Dropdown(["basic.4y", "high.school", "basic.6y", "basic.9y", "professional.course", "unknown", "university.degree", "illiterate", "tertiary", "secondary", "primary"], label="Education")
-
-        with gr.Row():
-            default = gr.Dropdown(["no", "unknown", "yes"], label="Default")
-            housing = gr.Dropdown(["no", "yes", "unknown"], label="Housing Loan")
-            loan = gr.Dropdown(["no", "yes", "unknown"], label="Personal Loan")
-            contact = gr.Dropdown(["telephone", "cellular", "unknown"], label="Contact Type")
-
-        with gr.Row():
-            month = gr.Dropdown(["may", "jun", "jul", "aug", "oct", "nov", "dec", "mar", "apr", "sep", "jan", "feb"], label="Month")
-            day_of_week = gr.Dropdown(["mon", "tue", "wed", "thu", "fri"], label="Day of Week")
-
-        with gr.Row():
-            duration = gr.Number(label="Call Duration (seconds)", value=100)
-            campaign = gr.Number(label="Number of Contacts during Campaign", value=1)
-            pdays = gr.Number(label="Days since Last Contact", value=999)
-            previous = gr.Number(label="Number of Contacts before Campaign", value=0)
-            poutcome = gr.Dropdown(["nonexistent", "failure", "success", "unknown", "other"], label="Previous Outcome")
-
-        with gr.Row():
-            emp_var_rate = gr.Number(label="Employment Variation Rate", value=1.1)
-            cons_price_idx = gr.Number(label="Consumer Price Index", value=93.994)
-            cons_conf_idx = gr.Number(label="Consumer Confidence Index", value=-36.4)
-            euribor3m = gr.Number(label="Euribor 3-Month Rate", value=4.857)
-            nr_employed = gr.Number(label="Number of Employees", value=5191.0)
-
-        predict_btn = gr.Button("Predict")
-        output = gr.JSON()
-
-        predict_btn.click(
-            predict, 
-            inputs=[age, job, marital, education, default, housing, loan, contact, month, day_of_week, duration, campaign, pdays, previous, poutcome, emp_var_rate, cons_price_idx, cons_conf_idx, euribor3m, nr_employed], 
-            outputs=output
-        )
-
-    return app
-
-# Launch the app
-if __name__ == "__main__":
-    app = gradio_interface()
-    app.launch()
+import gradio as gr
+import pandas as pd
+import joblib
+import os
+
+# Load the models
+preprocessor_path = os.path.join('.', 'preprocessor.joblib')
+model_path = os.path.join('.', 'Best_model.joblib')
+
+preprocessor = joblib.load(preprocessor_path)
+best_model = joblib.load(model_path)
+
+# Define prediction function
+def predict(age, job, marital, education, default, housing, loan, contact, month, day_of_week, duration, campaign, pdays, previous, poutcome, emp_var_rate, cons_price_idx, cons_conf_idx, euribor3m, nr_employed):
+    # Create a DataFrame for the input data
+    data = pd.DataFrame({
+        'age': [age],
+        'job': [job],
+        'marital': [marital],
+        'education': [education],
+        'default': [default],
+        'housing': [housing],
+        'loan': [loan],
+        'contact': [contact],
+        'month': [month],
+        'day_of_week': [day_of_week],
+        'duration': [duration],
+        'campaign': [campaign],
+        'pdays': [pdays],
+        'previous': [previous],
+        'poutcome': [poutcome],
+        'emp.var.rate': [emp_var_rate],
+        'cons.price.idx': [cons_price_idx],
+        'cons.conf.idx': [cons_conf_idx],
+        'euribor3m': [euribor3m],
+        'nr.employed': [nr_employed]
+    })
+
+    # Preprocess the data
+    preprocessed_data = preprocessor.transform(data)
+
+    # Make predictions
+    prediction = best_model.predict(preprocessed_data)
+    probability = best_model.predict_proba(preprocessed_data)
+
+    return {
+        "Prediction": prediction[0],
+        "Probability (Yes)": probability[0][1],
+        "Probability (No)": probability[0][0]
+    }
+
+# Define the interface
+def gradio_interface():
+    with gr.Blocks() as app:
+        gr.Markdown("# Bank Marketing Campaign Prediction")
+
+        with gr.Row():
+            age = gr.Number(label="Age", value=30)
+            job = gr.Dropdown(["housemaid", "services", "admin.", "blue-collar", "technician", "retired", "management", "unemployed", "self-employed", "unknown", "entrepreneur", "student"], label="Job")
+            marital = gr.Dropdown(["married", "single", "divorced", "unknown"], label="Marital Status")
+            education = gr.Dropdown(["basic.4y", "high.school", "basic.6y", "basic.9y", "professional.course", "unknown", "university.degree", "illiterate", "tertiary", "secondary", "primary"], label="Education")
+
+        with gr.Row():
+            default = gr.Dropdown(["no", "unknown", "yes"], label="Default")
+            housing = gr.Dropdown(["no", "yes", "unknown"], label="Housing Loan")
+            loan = gr.Dropdown(["no", "yes", "unknown"], label="Personal Loan")
+            contact = gr.Dropdown(["telephone", "cellular", "unknown"], label="Contact Type")
+
+        with gr.Row():
+            month = gr.Dropdown(["may", "jun", "jul", "aug", "oct", "nov", "dec", "mar", "apr", "sep", "jan", "feb"], label="Month")
+            day_of_week = gr.Dropdown(["mon", "tue", "wed", "thu", "fri"], label="Day of Week")
+
+        with gr.Row():
+            duration = gr.Number(label="Call Duration (seconds)", value=100)
+            campaign = gr.Number(label="Number of Contacts during Campaign", value=1)
+            pdays = gr.Number(label="Days since Last Contact", value=999)
+            previous = gr.Number(label="Number of Contacts before Campaign", value=0)
+            poutcome = gr.Dropdown(["nonexistent", "failure", "success", "unknown", "other"], label="Previous Outcome")
+
+        with gr.Row():
+            emp_var_rate = gr.Number(label="Employment Variation Rate", value=1.1)
+            cons_price_idx = gr.Number(label="Consumer Price Index", value=93.994)
+            cons_conf_idx = gr.Number(label="Consumer Confidence Index", value=-36.4)
+            euribor3m = gr.Number(label="Euribor 3-Month Rate", value=4.857)
+            nr_employed = gr.Number(label="Number of Employees", value=5191.0)
+
+        predict_btn = gr.Button("Predict")
+        output = gr.JSON()
+
+        predict_btn.click(
+            predict, 
+            inputs=[age, job, marital, education, default, housing, loan, contact, month, day_of_week, duration, campaign, pdays, previous, poutcome, emp_var_rate, cons_price_idx, cons_conf_idx, euribor3m, nr_employed], 
+            outputs=output
+        )
+
+    return app
+
+# Launch the app
+if __name__ == "__main__":
+    app = gradio_interface()
+    app.launch()