Update app.py

app.py

@@ -3,11 +3,12 @@ import joblib
 import numpy as np
 import pandas as pd
 from huggingface_hub import hf_hub_download
-from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder
 
-REPO_ID = "Hemg/modelxxx"
-MoDEL_FILENAME = "studentpredict.joblib"
-SCALER_FILENAME = "studentscaler.joblib"
+# Load the trained model and scaler objects from file
+REPO_ID = "Hemg/marketpredict"  # Hugging Face repo ID
+MoDEL_FILENAME = "stx.joblib"  # Model file name
+SCALER_FILENAME = "scaler.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))
@@ -24,100 +25,43 @@ def encode_categorical_columns(df):
 
     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]]
+# Define the prediction function
+def predict_performance(Year, Instagram_Advertising, Facebook_Advertising, Event_Expenses, Internet_Expenses):
+    # Prepare input data (represents independent variables for house prediction)
+    input_data = [[Year, Instagram_Advertising, Facebook_Advertising, Event_Expenses, Internet_Expenses]]
 
-        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 the feature names from the Gradio interface inputs
+    feature_names = ["Year", "Instagram_Advertising", "Facebook_Advertising", "Event_Expenses", "Internet_Expenses"]
 
-        # 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
+    # Create a Pandas DataFrame with the input data and feature names
+    input_df = pd.DataFrame(input_data, columns=feature_names)
 
-        # Create styled HTML output
-        # html_template = """
-        # <div style='text-align: center; padding: 20px;'>
-        #     <div style='font-family: Arial, sans-serif; font-size: 24px; margin-bottom: 15px;'>
-        #         Admission Probability: <span style='font-weight: bold;'>{:.1f}%</span>
-        #     </div>
-        #     <div style='{style}'>
-        #         {message}
-        #     </div>
-        # </div>
-        # """
- # Create styled HTML output
-        html_template = """
-        <div style='text-align: center; padding: 20px;'>
-            <div style='{style}'>
-                {message}
-            </div>
-        </div>
-        """
+    input_df = encode_categorical_columns(input_df)
 
+    # Scale the input data using the loaded scaler
+    scaled_input = scaler.transform(input_df)
 
-        if prediction_percentage > 50:
-            style = "font-family: Arial, sans-serif; font-size: 32px; color: #28a745; font-weight: bold;"
-            message = "High chance of admission"
-        elif prediction_percentage < 50:
-            style = "font-family: Arial, sans-serif; font-size: 32px; color: #dc3545; font-weight: bold; text-transform: uppercase;"
-            message = "Lower chance of admission"
-        else:  # exactly 50
-            style = "font-family: Arial, sans-serif; font-size: 32px; color: #ffc107; font-weight: bold;"
-            message = "Moderate chance of admission"
+    # Make predictions using the loaded model
+    prediction = model.predict(scaled_input)[0]
 
-        return html_template.format(prediction_percentage, style=style, message=message)
+    # Return the result as HTML with custom styling (green color and larger font)
+    return f'<p style="font-size: 24px; color: green;">Forecast no of. Students admission: {prediction:,.0f}</p>'
 
-    except Exception as e:
-        return f"<div style='color: red; font-family: Arial, sans-serif;'>Error in prediction: {str(e)}</div>"
-
-# Update the Gradio interface
+# Create the Gradio app
 iface = gr.Interface(
     fn=predict_performance,
-   inputs=[
-        gr.Radio(["Kathmandu", "Bhaktapur", "Lalitpur", "Kritipur"], label="Location",info="What is your current location?"),
-        gr.Slider(minimum=1000000, maximum=1700000,step=100000,label="College Fee", info="What 's the the total bachelor fee for the course you want to enroll?"),
-        gr.Radio(["Trinity", "CCRC", "KMC", "SOS", "ISMT", "St. Xavier's", "Everest", "Prime"], label="College", info="What is the name of the last college you attended?"),
-        gr.Slider(minimum=2, maximum=3, label="GPA", info="What is your GPA (Grade Point Average) of +2 ?"),
-        gr.Slider(minimum=2024, maximum=2026, step=1, label="Year", info="What is your intended year of admission?"),
-        #gr.Radio([2024, 2025, 2026], label="Year", info="What is your intended year of admission?")
-        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", info="Which course are you most interested in?"),
-        gr.Radio(["Science", "Management", "Humanities"], label="Faculty", info="what is your last stream ?"),
-        gr.Radio(["Event", "Facebook", "Instagram", "Offline", "Recommendation"], label="Source",info="How did you first hear about this college?"),
-        gr.Radio(["Yes", "No"], label="visited_college_for_inquery_only", info="Have you visited the college you're interested in for an inquiry or consultation?"),
-        gr.Radio(["Yes", "No"], label="attended_any_event", info="Have you attended any events organized by the college you're interested in?"),
-        gr.Radio(["New Year", "Dashain", "Orientation", "Fresher's Party", "Holi Festival", "Welcome Ceremony"], 
-                label="attended_event_name", info="If yes, which events did you attend?" ),
-        gr.Radio(["Ram", "Gita", "Manish", "Shyam", "Raj", "Hari", "Rina", "Shree"], label="Presenter", info="who is the counser that help you while in counseling?"),
-        gr.Radio(["Yes", "No"], label="visited_with_parents", info="Did you visit the college with your parents?")
+    inputs=[
+        gr.Slider(minimum=2024, maximum=2025, step=1, label="Year",info="The forecasted Year"),
+        gr.Slider(minimum=10000, maximum=45000, step=500, label="Instagram_Advertising", info="How much do you spend on Instagram ads Yearly($)?"),
+        gr.Slider(minimum=10000, maximum=75000, step=500, label="Facebook_Advertising", info="How much do you spend on Facebook ads Yearly($)?"),
+        gr.Slider(minimum=20000, maximum=100000, step=500, label="Event_Expenses", info="What’s your typical budget for events($)?"),
+        gr.Slider(minimum=5000, maximum=45000, step=500, label="Internet_Expenses", info="How much do you spend on internet Yearly($)?")
     ],
-
-
-    
-    outputs=gr.HTML(),  # Changed to HTML output
-    title="Student Admission Prediction",
-    description="Predict the probability of student admission",
-    css="body { font-family: Arial, sans-serif; }"
+    outputs=gr.HTML(),  # Specify the output as HTML
+    title="Student Admission Forecast",
+    description="Forecast of chances of student admission based on marketing expenditures"
 )
 
+# Run the app
 if __name__ == "__main__":
     iface.launch(share=True)