version 1

app.py

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+import gradio as gr
+import joblib
+
+# Load models
+models = {
+    "Logistic Regression": joblib.load("models/best_model.joblib"),
+    "Random Forest": joblib.load("models/random_forest_model.joblib"),
+    "KNN": joblib.load("models/trained_knn_model.joblib"),
+}
+
+# Load vectorizer
+vectorizer = joblib.load("models/vectorizer.joblib")
+
+# Define prediction function
+def predict_sentiment(review, model_name):
+    # Transform the review text using the vectorizer
+    processed_review = vectorizer.transform([review])
+
+    # Select the model
+    model = models[model_name]
+
+    # Make predictions
+    predicted_class = model.predict(processed_review)[0]
+    probabilities = model.predict_proba(processed_review)[0]
+
+    # Define sentiment labels
+    sentiment_labels = ["Negative Comment", "Positive Comment"]
+    predicted_label = sentiment_labels[predicted_class]
+
+    # Return probabilities as percentages
+    positive_percentage = probabilities[1] * 100
+    negative_percentage = probabilities[0] * 100
+
+    return predicted_label, positive_percentage, negative_percentage
+
+# Build Gradio interface
+with gr.Blocks() as interface:
+    gr.Markdown("<h1>Text Classification Models</h1>")
+    gr.Markdown("Choose a model and provide a review to see the sentiment analysis results with probabilities displayed as scales.")
+
+    with gr.Row():
+        with gr.Column():
+            review_input = gr.Textbox(label="Review Comment", placeholder="Type your comment here...")
+            model_selector = gr.Dropdown(
+                choices=list(models.keys()), label="Select Model", value="Logistic Regression"
+            )
+            submit_button = gr.Button("Submit")
+
+        with gr.Column():
+            sentiment_output = gr.Textbox(label="Predicted Sentiment Class", interactive=False)
+            positive_progress = gr.Slider(label="Positive Comment Percentage", minimum=0, maximum=100, interactive=False)
+            negative_progress = gr.Slider(label="Negative Comment Percentage", minimum=0, maximum=100, interactive=False)
+
+    submit_button.click(
+        predict_sentiment,
+        inputs=[review_input, model_selector],
+        outputs=[sentiment_output, positive_progress, negative_progress],
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()

models/best_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:32c6f8bb6849d5feac2973b3a7aa0c21aff2f66fe5f099b3f19c6d3eb1e19ed1
+size 12191

models/random_forest_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a4e926055ba3fcc2e850ac763a320d485b3339dd417ca9dddef507838e4f8204
+size 193449

models/trained_knn_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cdcbeb9d497a325a93fdcf5aa6f8ec0abd60f4496ecf38931c0406d653e34912
+size 49052

models/vectorizer.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ff59e97a4cb1106ae015d35f8166d6fee0eb6a00cb2adcc4fb91808ff1108f30
+size 17468