Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model_path = "yourusername/product-review-sentiment-analyzer"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+# Define sentiment labels
+labels = ["Negative", "Positive", "Neutral"]
+
+# Define prediction function
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    prediction = torch.argmax(probabilities, dim=-1).item()
+    
+    return {labels[i]: float(probabilities[0][i]) for i in range(len(labels))}
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(placeholder="Enter a product review..."),
+    outputs=gr.Label(num_top_classes=3),
+    title="Product Review Sentiment Analyzer",
+    description="Analyze the sentiment of product reviews as Positive, Negative, or Neutral."
+)
+
+# Launch app
+demo.launch()