Create app.py

app.py

@@ -0,0 +1,66 @@
+import torch
+from transformers import BertTokenizer
+from regression_models import BERTRegression
+
+max_len = 80
+
+# Load tokenizer
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+
+# Load model architecture
+bertregressor = BERTRegression()
+bertregressor.load_state_dict(torch.load('bert_regression_model.pth', map_location=torch.device('cpu')))
+bertregressor.eval()
+
+def predict_price(name, item_condition, category, brand_name, shipping_included, item_description):
+    print((name, item_condition, category, brand_name, shipping_included, item_description))
+    # Preprocess Input
+    if shipping_included:
+        shipping_str = "Includes Shipping"
+    else:
+        shipping_str = "No Shipping"
+        
+    combined = "Item Name: " + name + \
+            " Description: " + item_description + \
+            " Condition: " + item_condition + \
+            " Category: " + category + \
+            " Brand " + brand_name + \
+            " Shipping: " + shipping_str
+    
+    inputs = tokenizer.encode_plus(
+        combined,
+        None,
+        add_special_tokens=True,
+        max_length=max_len,
+        padding="max_length",
+        truncation=True,
+        return_tensors="pt"
+    )
+    
+    input_ids = inputs["input_ids"]
+    attention_mask = inputs["attention_mask"]
+    
+    with torch.no_grad():
+        output = bertregressor(input_ids, attention_mask)
+    
+    return output.item()
+
+    
+demo = gr.Interface(
+    
+    fn = predict_price,
+    
+    inputs = [gr.Textbox(label="Item Name"), 
+              gr.Dropdown(['Poor', 'Okay', 'Good', 'Excellent', 'Like New'], label="Item Condition", info="What condition is the item in?"),
+              gr.Textbox(label="Category on Mercari"),
+              gr.Textbox(label="Brand"),
+              gr.Checkbox(label="Shipping Included"),
+              gr.Textbox(label="Description")
+             ],
+    
+    #outputs = gr.Textbox()
+    outputs= gr.Number()
+)
+
+
+demo.launch()