app.py

# Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import gradio as gr

# Load the CSV data into a pandas DataFrame
data = pd.read_csv('dataset.csv')

# Split the data into features (X) and labels (y)
X = data.iloc[:, :-1]  # All columns except the last one
y = data.iloc[:, -1]  # Last column (placed or not)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a pipeline with a Random Forest Classifier
pipeline = Pipeline([
    ('scaler', StandardScaler()),  # Standardize features
    ('classifier', RandomForestClassifier())  # Random Forest Classifier
])

# Fit the pipeline to the training data
pipeline.fit(X_train, y_train)

# Make predictions on the testing data
y_pred = pipeline.predict(X_test)

# Calculate accuracy of the model
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

# Define the input and output types for Gradio
input_type = 'csv'
output_type = 'label'

# Define the function to make predictions using the trained model
def predict_placement(Internships, CGPA, HistoryOfBacklogs):
    # Create a DataFrame from the input data
    input_df = pd.DataFrame({'Internships': [Internships], 'CGPA': [CGPA], 'HistoryOfBacklogs': [HistoryOfBacklogs]})
    
    # Make a prediction using the trained model
    prediction = pipeline.predict(input_df)[0]
    
    # Return the predicted label
    return 'Placed' if prediction else 'Not Placed'

interface = gr.Interface(
    fn=predict_placement,
    inputs=["number", "number", "number"],
    outputs="text",
    title="Student Placement Prediction",
    description="Predict whether a student will be placed in a job based on their internships, CGPA, and history of backlogs."
)

# Launch the Gradio interface
interface.launch()