| |
|
|
| import gradio as gr |
|
|
| def predict_sentiment(text): |
| |
| seq = tokenizer.texts_to_sequences([text]) |
| padded = pad_sequences(seq, maxlen=x_train.shape[1]) |
| pred = lstm_model.predict(padded) |
| sentiment = "Positive" if pred[0][0] >= 0.5 else "Negative" |
| return sentiment |
|
|
| iface = gr.Interface( |
| fn=predict_sentiment, |
| inputs=gr.Textbox(lines=2, label="Entrez un tweet en français"), |
| outputs=gr.Textbox(label="Sentiment prédit"), |
| title="Analyse de sentiment de tweets français", |
| description="Entrez un tweet pour obtenir la prédiction du sentiment (Positive/Negative) par le modèle LSTM." |
| ) |
|
|
| iface.launch() |
|
|
|
|
| def predict_sentiment(text): |
| |
| seq = tokenizer.texts_to_sequences([text]) |
| padded = pad_sequences(seq, maxlen=x_train.shape[1]) |
| pred = gru_model.predict(padded) |
| sentiment = "Positive" if pred[0][0] >= 0.5 else "Negative" |
| return sentiment |
|
|
| iface = gr.Interface( |
| fn=predict_sentiment, |
| inputs=gr.Textbox(lines=2, label="Entrez un tweet en français"), |
| outputs=gr.Textbox(label="Sentiment prédit"), |
| title="Analyse de sentiment de tweets français", |
| description="Entrez un tweet pour obtenir la prédiction du sentiment (Positive/Negative) par le modèle LSTM." |
| ) |
|
|
| iface.launch() |
|
|
| import gradio as gr |
|
|
| def predict_sentiment(text): |
| |
| seq = tokenizer.texts_to_sequences([text]) |
| padded = pad_sequences(seq, maxlen=x_train.shape[1]) |
| pred = SimpleRNN_model.predict(padded) |
| sentiment = "Positive" if pred[0][0] >= 0.5 else "Negative" |
| return sentiment |
|
|
| iface = gr.Interface( |
| fn=predict_sentiment, |
| inputs=gr.Textbox(lines=2, label="Entrez un tweet en français"), |
| outputs=gr.Textbox(label="Sentiment prédit"), |
| title="Analyse de sentiment de tweets français", |
| description="Entrez un tweet pour obtenir la prédiction du sentiment (Positive/Negative) par le modèle LSTM." |
| ) |
|
|
| iface.launch() |
