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import gradio as gr
import pickle
from transformers import pipeline
def load_model(selected_model):
with open(selected_model, 'rb') as file:
loaded_model = pickle.load(file)
return loaded_model
def predict(model, text):
encoder = {
0:'assets/negative.jpeg',
1:'assets/neutral.jpeg',
2:'assets/positive.jpeg'
}
selected_model = None
with open('vectorizer.pkl', 'rb') as file:
vectorizer = pickle.load(file)
if 'Random Forest' == model:
selected_model = "models/rf_twitter.pkl"
elif 'Logistic Regression' == model:
selected_model = "models/lg_twitter.pkl"
elif 'Naive Bayes' == model:
selected_model = "models/nb_twitter.pkl"
elif 'Decision Tree' == model:
selected_model = "models/dt_twitter.pkl"
elif 'KNN' == model:
selected_model = "models/knn_twitter.pkl"
else:
selected_model = "models/lg_twitter.pkl"
loaded_model = load_model(selected_model)
text_vector = vectorizer.transform([text])
prediction = loaded_model.predict(text_vector)
return encoder[prediction[0]]
classifier = pipeline(task="zero-shot-classification", model="facebook/bart-large-mnli")
def analyze_sentiment(text):
results = classifier(text, ["positive", "negative", "neutral"], multi_label=True)
sentiment = max(results['labels'], key=results['scores'].__getitem__)
return sentiment
# models = gr.Radio(['Random Forest', 'Logistic Regression','Naive Bayes','Decision Tree','KNN'], label="Choose model")
# demo = gr.Interface(fn=predict, inputs=[models,"text"], outputs="image", title="Sentiment Analysis")
demo = gr.Interface(fn=analyze_sentiment, inputs="text", outputs="text", title="Sentiment Analysis")
demo.launch() |