Update app.py

app.py

@@ -1,62 +1,82 @@
+import numpy as np
+import pandas as pd
 import gradio as gr
-import joblib
 import pickle
-import pandas as pd
 
-# 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')
+# 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 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]
+# Define input and output functions for Gradio
+def predict_placement(option, degree_p, internship, DSA, java, management,
+                      leadership, communication, sales):
+    if option == "Fullstack":
+        new_data = pd.DataFrame(
+            {
+                'degree_p': degree_p,
+                'internship': internship,
+                'DSA': DSA,
+                'java': java,
+            },
+            index=[0])
+        prediction = rf_fullstk.predict(new_data)
+        probability = rf_fullstk.predict_proba(new_data)[0][1]
+    elif option == "Marketing":
+        new_data = pd.DataFrame(
+            {
+                'degree_p': degree_p,
+                'internship': internship,
+                'management': management,
+                'leadership': leadership,
+            },
+            index=[0])
+        prediction = rf_prodengg.predict(new_data)
+        probability = rf_prodengg.predict_proba(new_data)[0][1]
+    elif option == "Production Engineer":
+        new_data = pd.DataFrame(
+            {
+                'degree_p': degree_p,
+                'internship': internship,
+                'communication': communication,
+                'sales': sales,
+            },
+            index=[0])
+        prediction = rf_mkt.predict(new_data)
+        probability = rf_mkt.predict_proba(new_data)[0][1]
     else:
-        p = 0
-        prob = 0
+        return "Invalid option"
 
-    if p == 1:
-        result = f'Placed\nYou will be placed with a probability of {prob:.2f}'
+    if prediction == 1:
+        return f"Placed\nYou will be placed with a probability of {probability:.2f}"
     else:
-        result = 'Not Placed'
-    return result
+        return "Not Placed"
+
 
-# Create the Gradio interface
+# Create Gradio interface
 iface = gr.Interface(
     fn=predict_placement,
     inputs=[
-        gr.inputs.Slider(label='Degree Percentage', key='degree_p', minimum=0, maximum=100, default=75, step=1),
-        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')
+        gr.inputs.Dropdown(["Fullstack", "Marketing", "Production Engineer"],
+                           label="Select Option"),
+        gr.inputs.Number(label="Degree Percentage"),
+        gr.inputs.Number(label="Internship"),
+        gr.inputs.Checkbox(label="DSA"),
+        gr.inputs.Checkbox(label="Java"),
+        gr.inputs.Checkbox(label="Management"),
+        gr.inputs.Checkbox(label="Leadership"),
+        gr.inputs.Checkbox(label="Communication"),
+        gr.inputs.Checkbox(label="Sales"),
     ],
-    outputs=gr.outputs.Textbox(label='Placement Prediction')
+    outputs=gr.outputs.Textbox(label="Placement Prediction"),
+    title="Placement Prediction",
+    description=
+    "Predict the chances of placement for different job roles using machine learning models.",
 )
 
-# Run the Gradio app
-iface.launch()
+# Launch Gradio app
+iface.launch(share=True)