app.py

import gradio as gr
from joblib import load

# Define function to load SVM model
def load_svm_model(model_url):
    return load(model_url)

# Load the SVM model from Hugging Face
svm_model = load_svm_model("https://huggingface.co/ankitdotpy/SVM_model_by_Group12")

# Define function for Gradio interface using the loaded model
def predict_sentiment(text):
    # Load the SVM model
    clf_svm = svm_model

    # Vectorize the input text (assuming you already have the vectorizer)
    text_vector = vectorizer.transform([text])

    # Predict sentiment
    sentiment = clf_svm.predict(text_vector)[0]

    # Get probabilities for each class
    probabilities = clf_svm.predict_proba(text_vector)[0]

    # Convert probabilities to percentages
    percentages = [round(prob * 100, 2) for prob in probabilities]

    # Choose the sentiment label based on the predicted class
    if sentiment == "POSITIVE":
        return f"Positive ({percentages[1]}%)"
    elif sentiment == "NEUTRAL":
        return f"Neutral ({percentages[2]}%)"
    else:
        return f"Negative ({percentages[0]}%)"

# Create Gradio interface
iface = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(placeholder="Enter Text", lines=10, label="Enter your text here:"),
    outputs=gr.Textbox(label="Sentiment"),
    title="Sentiment Analysis using Hugging Face Model",
    description="Enter text and predict sentiment"
)

iface.launch()