Update app.py

app.py

@@ -1,29 +1,63 @@
-from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, pipeline
+# Import necessary libraries
+from transformers import pipeline
 import gradio as gr
 
-# Load tokenizer and model explicitly
-tokenizer = AutoTokenizer.from_pretrained("raphgonda/FilipinoShopping")
-model = TFAutoModelForSequenceClassification.from_pretrained("raphgonda/FilipinoShopping")
-
-# Create a pipeline
-pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, framework="tf")
+# Load the Filipino sentiment analysis model
+pipe = pipeline("text-classification", model="raphgonda/FilipinoShopping")
 
 # Define the sentiment analysis function
 def analyze_sentiment(text):
     try:
-        result = pipe(text)
-        label = result[0]["label"]
-        score = round(result[0]["score"] * 100, 2)  # Convert score to percentage
+        # Predict sentiment using the model
+        results = pipe(text)
+        # Extract label and score
+        label = results[0]["label"]
+        score = round(results[0]["score"] * 100, 2)  # Convert score to percentage
         return label, f"{score}%"
     except Exception as e:
-        return "Error", str(e)
+        return "Error", "N/A"
+
+# Create a Gradio interface with custom UI
+with gr.Blocks() as interface:
+    gr.Markdown("<h1 style='text-align: center;'>Filipino Sentiment Analysis</h1>")
+    gr.Markdown("<p style='text-align: center;'>Enter text in Filipino to analyze its sentiment.</p>")
+
+    with gr.Row():
+        input_text = gr.Textbox(
+            label="Enter text to analyze its sentiment",
+            placeholder="Type your text here...",
+        )
+
+    with gr.Row():
+        submit_btn = gr.Button("Submit")
+        clear_btn = gr.Button("Clear")
+    
+    sentiment_label = gr.Textbox(label="Sentiment Label", interactive=False, visible=True)
+    
+    with gr.Row():
+        emotion_score = gr.Textbox(label="Emotion Score", interactive=False)
+
+    examples = gr.Examples(
+        examples=[
+            ["Okay ang aesthetic"],
+            ["Mabagal ang delivery"],
+            ["Napakaganda ng serbisyo!"],
+            ["Ang pangit ng produkto."]
+        ],
+        inputs=input_text,
+    )
 
-# Gradio app
-interface = gr.Interface(
-    fn=analyze_sentiment,
-    inputs=gr.Textbox(label="Enter Filipino Text"),
-    outputs=[gr.Textbox(label="Sentiment"), gr.Textbox(label="Emotion Score")],
-    title="Filipino Sentiment Analysis"
-)
+    # Define the function connection
+    submit_btn.click(
+        analyze_sentiment,
+        inputs=[input_text],
+        outputs=[sentiment_label, emotion_score],
+    )
+    clear_btn.click(
+        lambda: ("", ""),
+        inputs=[],
+        outputs=[sentiment_label, emotion_score],
+    )
 
+# Launch the app
 interface.launch()