Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer
+from model import SentimentClassifier
+
+model_state_dict = torch.load('sentiment_model.pth')
+model = SentimentClassifier(2)
+model.load_state_dict(model_state_dict)
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+
+
+def preprocess(text):
+    inputs = tokenizer(text, padding='max_length',
+                       truncation=True, max_length=512, return_tensors='pt')
+    return inputs
+# Define a function to use the model to make predictions
+def predict():
+    review = request.form['review']
+    inputs = preprocess(review)
+    with torch.no_grad():
+        outputs = model(inputs['input_ids'], inputs['attention_mask'])
+        predicted_class = torch.argmax(outputs[0]).item()
+    if(predicted_class==0):
+        return "It was a negative review"
+    return "It was a positive review"
+
+# Create a Gradio interface
+input_text = gr.inputs.Textbox(label="Input Text")
+output_text = gr.outputs.Textbox(label="Output Text")
+interface = gr.Interface(fn=predict, inputs=input_text, outputs=output_text)
+
+# Run the interface
+interface.test_launch()