ignored

.gitignore

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+# Ignore Python cache files
+__pycache__/
+
+# Ignore log files
+*.log
+
+# Ignore virtual environment
+venv/
+
+# Ignore compiled Python files
+*.pyc
+*.pyo
+
+# Ignore environment-specific files
+.env

app.py

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+# pip install scikit-learn
+
+#
+import gradio as gr
+import pandas as pd
+import pickle
+# from sklearn.pipeline import Pipeline
+# from sklearn.ensemble import RandomForestClassifier
+# from sklearn.preprocessing import StandardScaler, LabelEncoder
+# from sklearn.impute import SimpleImputer
+# from imblearn.over_sampling import RandomOverSampler
+# from sklearn.preprocessing import FunctionTransformer
+import joblib
+
+xtrain= pd.read_csv('Xtrains.csv')
+ytrain=pd.read_csv('Ytrains.csv')
+
+# Loading Models
+with open("model.pkl", "rb") as f:
+	clf  = pickle.load(f)
+
+clf.fit(xtrain, ytrain.values.ravel())
+
+tenure_labels = {
+    0: "3-6 months",
+    1: "6-9 months",
+    2: "9-12 months",
+    3: "12-15 months",
+    4: "15-18 months",
+    5: "18-21 months",
+    6: "21-24 months",
+    7: "> 24 months"
+}
+
+# Reverse the mapping for predictions
+tenure_values = {v: k for k, v in tenure_labels.items()}
+
+def predict(tenure, montant, freq_rech, revenue, arpu, freq, data_vol, on_net, orange, tigo,  freq_top_pack, regularity):
+
+    tenure_value = tenure_values[tenure]
+
+
+
+    input_df = pd.DataFrame({
+        'TENURE': [tenure_value],
+        'MONTANT': [montant],
+        'FREQUENCE_RECH': [freq_rech],
+        'REVENUE': [revenue],
+        'ARPU_SEGMENT': [arpu],
+        'FREQUENCE': [freq],
+        'DATA_VOLUME': [data_vol],
+        'ON_NET': [on_net],
+        'ORANGE': [orange],
+        'TIGO': [tigo],
+        'REGULARITY':[regularity],
+        'FREQ_TOP_PACK': [freq_top_pack]
+    })
+
+    prediction = clf.predict(input_df)
+
+
+
+    churn_label = "Customer will churn" if prediction == 1 else "Customer will not churn"
+    result = {
+        'text': churn_label,  # Use the churn label as 'text'
+        'entities': []  # You can leave 'entities' as an empty list if no entities need highlighting
+    }
+    print(result)
+    return result
+
+
+
+# Create a dropdown menu with labels
+tenure_dropdown = gr.inputs.Dropdown(list(tenure_labels.values()),  label="TENURE")
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        tenure_dropdown,  # Dropdown instead of slider
+        #gr.inputs.Slider(minimum=1, maximum=7, label="TENURE"),
+        gr.inputs.Slider(minimum=20, maximum=470000, label="MONTANT"),
+        gr.inputs.Slider(minimum=1, maximum=131, label="FREQUENCE_RECH"),
+        gr.inputs.Slider(minimum=1, maximum=530000, label="REVENUE"),
+        gr.inputs.Slider(minimum=0, maximum=2453, label="ARPU_SEGMENT"),
+        gr.inputs.Slider(minimum=1, maximum=91, label="FREQUENCE"),
+        gr.inputs.Slider(minimum=1, maximum=1702309, label="DATA_VOLUME"),
+        gr.inputs.Slider(minimum=0, maximum=51000, label="ON_NET"),
+        gr.inputs.Slider(minimum=0, maximum=12040, label="ORANGE"),
+        gr.inputs.Slider(minimum=0, maximum=4174, label="TIGO"),
+        gr.inputs.Slider(minimum=0, maximum=624, label="FREQ_TOP_PACK"),
+         gr.inputs.Slider(minimum=0, maximum=62, label="REGULARITY")
+    ],
+
+    outputs=output,
+    title="Team Paris Customer Churn Prediction App",
+    description="Let's Get Started With Some Predictions!"
+)
+
+iface.launch()