Create app.py

app.py

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+# Import necessary libraries
+from transformers import pipeline
+import gradio as gr
+
+# Load the Filipino sentiment analysis model
+pipe = pipeline("text-classification", model="raphgonda/FilipinoShopping")
+
+# Define the sentiment analysis function
+def analyze_sentiment(text):
+    try:
+        # 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", "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,
+    )
+
+    # 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()