Create app1.py

app1.py

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+import gradio as gr
+import joblib
+import numpy as np
+import pandas as pd
+from huggingface_hub import hf_hub_download
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+
+REPO_ID = "Hemg/modelxxx"
+MoDEL_FILENAME = "studentpredict.joblib"
+SCALER_FILENAME = "studentscaler.joblib"
+
+model = joblib.load(hf_hub_download(repo_id=REPO_ID, filename=MoDEL_FILENAME))
+scaler = joblib.load(hf_hub_download(repo_id=REPO_ID, filename=SCALER_FILENAME))
+
+def encode_categorical_columns(df):
+    label_encoder = LabelEncoder()
+    ordinal_columns = df.select_dtypes(include=['object']).columns
+
+    for col in ordinal_columns:
+        df[col] = label_encoder.fit_transform(df[col])
+
+    nominal_columns = df.select_dtypes(include=['object']).columns.difference(ordinal_columns)
+    df = pd.get_dummies(df, columns=nominal_columns, drop_first=True)
+
+    return df
+
+def predict_performance(Location, College_Fee, College, GPA, Year, Course_Interested, Faculty, Source, 
+                       Visited_College_for_Inquiry_Only, Event, Attended_Any_Events, 
+                       Presenter, Visited_Parents):
+    try:
+        input_data = [[Location, College_Fee, College, GPA, Year, Course_Interested, Faculty, Source,
+                    Visited_College_for_Inquiry_Only, Event, Attended_Any_Events,
+                    Presenter, Visited_Parents]]
+
+        feature_names = ["Location", "College Fee", "College", "GPA", "Year", "Course Interested",
+                     "Faculty", "Source", "Visited College for Inquiry Only", "Event",
+                     "Attended Any Events", "Presenter", "Visited Parents"]
+        
+        input_df = pd.DataFrame(input_data, columns=feature_names)
+        df = encode_categorical_columns(input_df)
+        df = df.reindex(columns=scaler.feature_names_in_, fill_value=0)
+        scaled_input = scaler.transform(df)
+
+        # Get probability prediction
+        probabilities = model.predict_proba(scaled_input)[0]
+        # Take the probability of positive class (usually index 1)
+        admission_probability = probabilities[1]
+        
+        # Ensure the probability is between 0 and 1
+        admission_probability = np.clip(admission_probability, 0, 1)
+        
+        # Convert to percentage
+        prediction_percentage = admission_probability * 100
+
+        # Format the output
+        confidence_level = ""
+        if prediction_percentage >= 75:
+            confidence_level = "High chance of admission"
+        elif prediction_percentage >= 50:
+            confidence_level = "Moderate chance of admission"
+        else:
+            confidence_level = "Lower chance of admission"
+
+        return f"Chance of Admission: {prediction_percentage:.1f}%\n{confidence_level}"
+
+    except Exception as e:
+        return f"Error in prediction: {str(e)}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_performance,
+    inputs=[
+        gr.Radio(["Kathmandu", "Bhaktapur", "Lalitpur", "Kritipur"], label="Location"),
+        gr.Slider(minimum=1000000, maximum=1700000, label="College Fee"),
+        gr.Radio(["Trinity", "CCRC", "KMC", "SOS", "ISMT", "St. Xavier's", "Everest", "Prime"], label="College"),
+        gr.Slider(minimum=2, maximum=3, label="GPA"),
+        gr.Slider(minimum=2020, maximum=2024, step=1, label="Year"),
+        gr.Radio(["MSc IT & Applied Security", "BSc (Hons) Computing", "BSc (Hons) Computing with Artificial Intelligence", 
+                 "BSc (Hons) Computer Networking & IT Security", "BSc (Hons) Multimedia Technologies", "MBA", 
+                 "BA (Hons) Accounting &  Finance", "BA (Hons) Business Administration"], label="Course_Interested"),
+        gr.Radio(["Science", "Management", "Humanities"], label="Faculty"),
+        gr.Radio(["Event", "Facebook", "Instagram", "Offline", "Recommendation"], label="Source"),
+        gr.Radio(["Yes", "No"], label="visited_college_for_inquery_only"),
+        gr.Radio(["New Year", "Dashain", "Orientation", "Fresher's Party", "Holi Festival", "Welcome Ceremony"], 
+                label="attended_event_name"),
+        gr.Radio(["Yes", "No"], label="attended_any_event"),
+        gr.Radio(["Ram", "Gita", "Manish", "Shyam", "Raj", "Hari", "Rina", "Shree"], label="Presenter"),
+        gr.Radio(["Yes", "No"], label="visited_with_parents")
+    ],
+    outputs="text",
+    title="Student Admission Prediction",
+    description="Predict the probability of student admission"
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)