Create app.py

app.py

@@ -0,0 +1,115 @@
+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, OneHotEncoder, LabelEncoder
+
+# Load the trained model and scaler objects from file
+
+
+REPO_ID = "Hemg/modelxxx" # hugging face  repo ID
+MoDEL_FILENAME = "studentpredict.joblib" # model file name
+SCALER_FILENAME ="studentscaler.joblib" # scaler file name
+
+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
+
+# Define the prediction function
+def predict_performance(Location, College_Fee, GPA, Year, Course_Interested, College,
+    Faculty, Source, visited_college_for_inquiry_only, attended_event_name,
+    attended_any_events, Presenter, visited_with_parents):
+    
+    # Debug print 1: Print all input values
+    print("\nInput Values:")
+    print(f"Location: {Location}")
+    print(f"College_Fee: {College_Fee}")
+    print(f"GPA: {GPA}")
+    print(f"Year: {Year}")
+    print(f"Course_Interested: {Course_Interested}")
+    print(f"Faculty: {Faculty}")
+    print(f"Source: {Source}")
+    print(f"Visited for inquiry: {visited_college_for_inquiry_only}")
+    print(f"Event name: {attended_event_name}")
+    print(f"Attended events: {attended_any_events}")
+    print(f"Presenter: {Presenter}")
+    print(f"Visited with parents: {visited_with_parents}")
+
+    input_data = [[Location, College_Fee, GPA, Year, Course_Interested, College,
+        Faculty, Source, visited_college_for_inquiry_only, attended_event_name,
+        attended_any_events, Presenter, visited_with_parents]]
+
+    feature_names = ["Location", "College_Fee", "GPA", "Year", "Course_Interested", "College",
+        "Faculty", "Source", "visited_college_for_inquiry_only", "attended_event_name",
+        "attended_any_events", "Presenter", "visited_with_parents"]
+    
+    input_df = pd.DataFrame(input_data, columns=feature_names)
+    
+    # Debug print 2: Show DataFrame before encoding
+    print("\nDataFrame before encoding:")
+    print(input_df)
+    
+    df = encode_categorical_columns(input_df)
+    
+    # Debug print 3: Show DataFrame after encoding
+    print("\nDataFrame after encoding:")
+    print(df)
+    
+    scaled_input = scaler.transform(df)
+    
+    # Debug print 4: Show scaled input
+    print("\nScaled input:")
+    print(scaled_input)
+    
+    prediction = model.predict(scaled_input)[0]
+    
+    # Debug print 5: Show prediction details
+    print("\nPrediction details:")
+    print(f"Raw prediction: {prediction}")
+    prediction_probability = 1 / (1 + np.exp(-prediction))
+    print(f"Probability: {prediction_probability}")
+    prediction_percentage = prediction_probability * 100
+    print(f"Percentage: {prediction_percentage}")
+    
+    return f"Chance of Admission: {prediction_percentage:.1f}%"
+ 
+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.Slider(minimum=2, maximum=3, label="GPA"),  # Fixed GPA input
+        gr.Slider(minimum=2019, maximum=2025, 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"),  # Changed from Slider to Radio
+        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="chances of student admission",
+    description="chances of student admission"
+)
+
+# Run the app
+if __name__ == "__main__":
+    iface.launch(share=True)