Update app.py

app.py

@@ -1,3 +1,35 @@
+# import gradio as gr
+
+# gr.load("models/AnkitAI/reviews-roberta-base-sentiment-analysis").launch()
+
 import gradio as gr
+from transformers import RobertaTokenizer, RobertaForSequenceClassification
+import torch
+
+# Load the model and tokenizer
+model_name = "AnkitAI/reviews-roberta-base-sentiment-analysis"
+model = RobertaForSequenceClassification.from_pretrained(model_name)
+tokenizer = RobertaTokenizer.from_pretrained(model_name)
+
+# Define a function to perform sentiment analysis and map labels
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_id = torch.argmax(logits, dim=1).item()
+    
+    # Map class id to label
+    labels = ["Negative", "Positive"]
+    return labels[predicted_class_id]
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter a review here..."),
+    outputs="text",
+    title="Reviews Sentiment Analysis",
+    description="Enter an Amazon review to see if it is positive or negative."
+)
 
-gr.load("models/AnkitAI/reviews-roberta-base-sentiment-analysis").launch()
+# Launch the interface
+interface.launch()