Update app.py

app.py

@@ -1,94 +1,62 @@
 import gradio as gr
-import numpy as np
-import pandas as pd
+import joblib
 import pickle
+import pandas as pd
 
-# Load trained models
-with open('rf_hacathon_fullstk.pkl', 'rb') as f1:
-    rf_fullstk = pickle.load(f1)
-with open('rf_hacathon_prodengg.pkl', 'rb') as f2:
-    rf_prodengg = pickle.load(f2)
-with open('rf_hacathon_mkt.pkl', 'rb') as f3:
-    rf_mkt = pickle.load(f3)
-
-# Define prediction function
-def predict_placed(degree_p, internship, DSA, java, management, leadership, communication, sales, model_name):
-    if model_name == 'Full Stack':
-        new_data = pd.DataFrame({
-            'degree_p': degree_p,
-            'internship': internship,
-            'DSA': DSA,
-            'java': java,
-            'management': 0,
-            'leadership': 0,
-            'communication': 0,
-            'sales': 0
-        }, index=[0])
-        model = rf_fullstk
-    elif model_name == 'Product Engineering':
-        new_data = pd.DataFrame({
-            'degree_p': degree_p,
-            'internship': internship,
-            'DSA': 0,
-            'java': 0,
-            'management': management,
-            'leadership': leadership,
-            'communication': 0,
-            'sales': 0
-        }, index=[0])
-        model = rf_prodengg
-    elif model_name == 'Marketing':
-        new_data = pd.DataFrame({
-            'degree_p': degree_p,
-            'internship': internship,
-            'DSA': 0,
-            'java': 0,
-            'management': 0,
-            'leadership': 0,
-            'communication': communication,
-            'sales': sales
-        }, index=[0])
-        model = rf_mkt
-
-    prediction = model.predict(new_data)
-    probability = model.predict_proba(new_data)[0][1]
+# Load the trained models
+rf_fullstk = joblib.load('rf_hacathon_fullstk.pkl')
+rf_prodengg = joblib.load('rf_hacathon_prodengg.pkl')
+rf_mkt = joblib.load('rf_hacathon_mkt.pkl')
+
+# Define the prediction function
+def predict_placement(degree_p, internship, DSA, java, management, leadership, communication, sales):
+    # Create a new data frame from the input
+    new_data = pd.DataFrame({
+        'degree_p': degree_p,
+        'internship': internship,
+        'DSA': DSA,
+        'java': java,
+        'management': management,
+        'leadership': leadership,
+        'communication': communication,
+        'sales': sales
+    }, index=[0])
+
+    # Predict placement using the respective model
+    if management == 1 and leadership == 0:
+        p = rf_prodengg.predict(new_data)[0]
+        prob = rf_prodengg.predict_proba(new_data)[0][1]
+    elif DSA == 1 and java == 0:
+        p = rf_fullstk.predict(new_data)[0]
+        prob = rf_fullstk.predict_proba(new_data)[0][1]
+    elif communication == 0 and sales == 1:
+        p = rf_mkt.predict(new_data)[0]
+        prob = rf_mkt.predict_proba(new_data)[0][1]
+    else:
+        p = 0
+        prob = 0
 
-    if prediction == 1:
-        result = 'Placed'
-        probability_message = f"You will be placed with a probability of {probability:.2f}"
+    if p == 1:
+        result = f'Placed\nYou will be placed with a probability of {prob:.2f}'
     else:
         result = 'Not Placed'
-        probability_message = ""
-
-    return result, probability_message
-
-# Create Gradio interface
-inputs = [
-    gr.inputs.Number(label='Degree Percentage'),
-    gr.inputs.Radio(label='Internship', choices=[0, 1]),
-    gr.inputs.Radio(label='Data Structures & Algorithms', choices=[0, 1]),
-    gr.inputs.Radio(label='Java', choices=[0, 1]),
-    gr.inputs.Radio(label='Management Skills', choices=[0, 1]),
-    gr.inputs.Radio(label='Leadership Skills', choices=[0, 1]),
-    gr.inputs.Radio(label='Communication Skills', choices=[0, 1]),
-    gr.inputs.Radio(label='Sales Skills', choices=[0, 1]),
-    gr.inputs.Dropdown(label='Model Name', choices=['Full Stack', 'Product Engineering', 'Marketing'])
-]
-
-outputs = [
-    gr.outputs.Textbox(label='Placement Result'),
-    gr.outputs.Textbox(label='Placement Probability')
-]
-
-app = gr.Interface(
-    fn=predict_placed,
-    inputs=inputs,
-    outputs=outputs,
-    title='Placement Prediction',
-    description='Predict placement outcome based on given inputs',
-    allow_flagging=False
+    return result
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_placement,
+    inputs=[
+        gr.inputs.Slider(label='Degree Percentage', minimum=0, maximum=100, default=75, step=1, key='degree_p'),
+        gr.inputs.Checkbox(label='Internship', default=True, key='internship'),
+        gr.inputs.Checkbox(label='DSA', default=True, key='DSA'),
+        gr.inputs.Checkbox(label='Java', default=False, key='java'),
+        gr.inputs.Checkbox(label='Management', default=False, key='management'),
+        gr.inputs.Checkbox(label='Leadership', default=False, key='leadership'),
+        gr.inputs.Checkbox(label='Communication', default=False, key='communication'),
+        gr.inputs.Checkbox(label='Sales', default=False, key='sales')
+    ],
+    outputs=gr.outputs.Textbox(label='Placement Prediction')
 )
 
-# Run the app
-if __name__ == '__main__':
-    app.run()
+# Run the Gradio app
+iface.launch()