Update app.py

app.py

@@ -39,7 +39,7 @@ def classify_review(user_review):
             confidence = sentiment_result['score']
             sentiment_based_classification = f"Model prediction: {sentiment} with confidence: {confidence:.2f}"
 
-            return f"{rating_based_classification}\n{sentiment_based_classification}\nMatching Score: {best_score}%"
+            return f"{rating_based_classification}\n{sentiment_based_classification}\nMatching Score: {best_score}%\nBest Match\n{best_match}"
         else:
             return "Review not found in the dataset."
 
@@ -58,11 +58,7 @@ def plot_rating_distribution():
 
 # Function to allow users to preview the dataset (first 10 rows)
 def preview_dataset():
-    return reviews_df.head(10)  # Return the first 10 rows of the dataset
-
-# Function to download the dataset
-def download_dataset():
-    return 'Restaurant_reviews.csv'  # Provide the path to the dataset for download
+    return reviews_df.head(15)  # Return the first 10 rows of the dataset
 
 # Create the Gradio interface for classifying reviews
 review_interface = gr.Interface(
@@ -73,6 +69,14 @@ review_interface = gr.Interface(
     description="Enter a restaurant review. The system will classify it based on the dataset rating and use a sentiment analysis model."
 )
 
+# Create the Gradio interface for classifying reviews
+review_interface = gr.Interface(
+    fn=classify_review,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your review here", label="Reviews"),
+    outputs="text",
+    title="Review Classifier Based on Rating and Hugging Face Model",
+    description="Enter a restaurant review. The system will classify it based on the dataset rating and use a sentiment analysis model."
+)
 # Create the Gradio interface for plotting the rating distribution
 plot_interface = gr.Interface(
     fn=plot_rating_distribution,
@@ -91,20 +95,13 @@ preview_interface = gr.Interface(
     description="Displays the first 10 rows of the dataset for preview."
 )
 
-# Create the Gradio interface for downloading the dataset
-download_interface = gr.Interface(
-    fn=download_dataset,
-    inputs=[],
-    outputs=gr.File(),
-    title="Download Restaurant Reviews Dataset",
-    description="Download the full restaurant reviews dataset in CSV format."
-)
+
 
 # Combine all interfaces (Review Classifier, Rating Distribution, Dataset Preview, Dataset Download) into tabs
 tabbed_interface = gr.TabbedInterface(
-    [review_interface, plot_interface, preview_interface, download_interface],
-    ["Review Classifier", "Rating Distribution", "Dataset Preview", "Download Dataset"]
+    [review_interface, plot_interface, preview_interface],
+    ["Review Classifier", "Rating Distribution", "Dataset Preview", ""]
 )
 
 # Launch the Gradio interface
-tabbed_interface.launch()
+tabbed_interface.launch()