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Add Gradio demo for logistic regression model, first try

app.py

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+import gradio as gr
+from datasets import load_dataset
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+
+# Load the dataset and prepare the model (as you already did)
+dataset = load_dataset("nhull/tripadvisor-split-dataset")
+train_data = dataset['train']
+val_data = dataset['validation']
+test_data = dataset['test']
+
+# Prepare data and labels
+X_train, y_train = train_data['review'], train_data['label']
+X_val, y_val = val_data['review'], val_data['label']
+X_test, y_test = test_data['review'], test_data['label']
+
+# Vectorize the text using TF-IDF
+vectorizer = TfidfVectorizer(max_features=10000)
+X_train_vec = vectorizer.fit_transform(X_train)
+X_val_vec = vectorizer.transform(X_val)
+X_test_vec = vectorizer.transform(X_test)
+
+# Train the logistic regression model
+model = LogisticRegression(max_iter=1000)
+model.fit(X_train_vec, y_train)
+
+# Define the prediction function
+def predict_sentiment(text):
+    text_vec = vectorizer.transform([text])
+    prediction = model.predict(text_vec)[0]
+    return f"Predicted label: {prediction}"
+
+# Create the Gradio interface
+iface = gr.Interface(fn=predict_sentiment,
+                     inputs=gr.Textbox(label="Enter a review text", placeholder="Type your review here..."),
+                     outputs=gr.Textbox(label="Predicted label"),
+                     live=True)
+
+# Launch the interface
+iface.launch()

requirements.txt

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+gradio
+scikit-learn
+datasets