Update app.py

app.py

@@ -1,64 +1,29 @@
-# Import necessary libraries
-from transformers import pipeline
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification, pipeline
+import gradio as gr
+
+# Load tokenizer and model explicitly
+tokenizer = AutoTokenizer.from_pretrained("raphgonda/FilipinoShopping")
+model = TFAutoModelForSequenceClassification.from_pretrained("raphgonda/FilipinoShopping")
 
-# Load the Filipino sentiment analysis model using TensorFlow weights
-pipe = pipeline("text-classification", model="raphgonda/FilipinoShopping", framework="tf", from_tf=True)
+# Create a pipeline
+pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, framework="tf")
 
 # 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
+        result = pipe(text)
+        label = result[0]["label"]
+        score = round(result[0]["score"] * 100, 2)  # Convert score to percentage
         return label, f"{score}%"
     except Exception as e:
-        return "Error", "N/A"
-
-# Deploy with Gradio (same as before)
-import gradio as gr
-
-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 using the FilipinoShopping model.</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,
-    )
+        return "Error", str(e)
 
-    # 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],
-    )
+# 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"
+)
 
-# Launch the app
 interface.launch()