Upload app.py

app.py

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+
+import pandas as pd
+import numpy as np
+from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pickle
+import gradio as gr
+import os
+
+# Load the model
+model_path = 'career_prediction_model.pkl'
+with open(model_path, 'rb') as f:
+    saved_data = pickle.load(f)
+
+model = saved_data['model']
+label_encoders = saved_data['label_encoders']
+target_encoder = saved_data['target_encoder']
+features = saved_data['features']
+target = 'What would you like to become when you grow up'
+
+# Function for individual prediction
+def predict_career(work_env, academic_perf, motivation, leadership, tech_savvy, preferred_subjects, gender, risk_taking=5, financial_stability=5, work_exp="No Experience"):
+    # Prepare input data
+    input_data = pd.DataFrame({
+        'Preferred Work Environment': [work_env],
+        'Academic Performance (CGPA/Percentage)': [float(academic_perf)],
+        'Motivation for Career Choice ': [motivation],  # Note the space at the end
+        'Leadership Experience': [leadership],
+        'Tech-Savviness': [tech_savvy],
+        'Preferred Subjects in Highschool/College': [preferred_subjects],  # New feature
+        'Gender': [gender],  # New feature
+        'Risk-Taking Ability ': [float(risk_taking)],  # Note the space at the end
+        'Financial Stability - self/family (1 is low income and 10 is high income)': [float(financial_stability)],
+        'Previous Work Experience (If Any)': [work_exp]
+    })
+    
+    # Encode categorical features
+    for feature in features:
+        if feature in input_data.columns:
+            if feature in label_encoders and input_data[feature].dtype == 'object':
+                try:
+                    input_data[feature] = label_encoders[feature].transform(input_data[feature])
+                except ValueError:
+                    # Handle unknown categories
+                    print(f"Warning: Unknown category in {feature}. Using most frequent category.")
+                    input_data[feature] = 0  # Default to first category
+        else:
+            print(f"Warning: Feature {feature} not found in input data.")
+    
+    # Make prediction
+    prediction = model.predict(input_data)[0]
+    predicted_career = target_encoder.inverse_transform([int(prediction)])[0]
+    
+    # Get probabilities for all classes
+    if hasattr(model, 'predict_proba'):
+        probabilities = model.predict_proba(input_data)[0]
+        class_probs = {target_encoder.inverse_transform([i])[0]: prob 
+                      for i, prob in enumerate(probabilities)}
+        sorted_probs = dict(sorted(class_probs.items(), key=lambda x: x[1], reverse=True))
+        
+        result = f"Predicted career: {predicted_career}\n\nProbabilities:\n"
+        for career, prob in sorted_probs.items():
+            result += f"{career}: {prob:.2f}\n"
+        return result
+    else:
+        return f"Predicted career: {predicted_career}"
+
+# Get unique values for dropdowns
+work_env_options = list(label_encoders['Preferred Work Environment'].classes_)
+motivation_options = list(label_encoders['Motivation for Career Choice '].classes_)
+leadership_options = list(label_encoders['Leadership Experience'].classes_)
+tech_savvy_options = list(label_encoders['Tech-Savviness'].classes_)
+
+# Get options for new features with error handling
+subject_options = []
+if 'Preferred Subjects in Highschool/College' in label_encoders:
+    subject_options = list(label_encoders['Preferred Subjects in Highschool/College'].classes_)
+else:
+    # Default options if not in the model
+    subject_options = ["Science", "Commerce", "Arts", "Unknown"]
+
+gender_options = []
+if 'Gender' in label_encoders:
+    gender_options = list(label_encoders['Gender'].classes_)
+else:
+    # Default options if not in the model
+    gender_options = ["Male", "Female", "Other"]
+
+# Get work experience options if available
+work_exp_options = []
+if 'Previous Work Experience (If Any)' in label_encoders:
+    work_exp_options = list(label_encoders['Previous Work Experience (If Any)'].classes_)
+else:
+    work_exp_options = ["No Experience", "Internship", "Part Time", "Full Time"]
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_career,
+    inputs=[
+        gr.Dropdown(work_env_options, label="Preferred Work Environment"),
+        gr.Number(label="Academic Performance (CGPA/Percentage)", minimum=0, maximum=10),
+        gr.Dropdown(motivation_options, label="Motivation for Career Choice"),
+        gr.Dropdown(leadership_options, label="Leadership Experience"),
+        gr.Dropdown(tech_savvy_options, label="Tech-Savviness"),
+        gr.Dropdown(subject_options, label="Preferred Subjects"),
+        gr.Dropdown(gender_options, label="Gender"),
+        gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Risk-Taking Ability"),
+        gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Financial Stability"),
+        gr.Dropdown(work_exp_options, label="Previous Work Experience")
+    ],
+    outputs="text",
+    title="Career Prediction Model",
+    description="Enter your details to predict your future career path",
+    theme="huggingface"
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()