Update app.py

app.py

@@ -1,6 +1,10 @@
 import torch
 from peft import PeftModel, PeftConfig
 from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+
+# Import the specific Gradio components
+from gradio import Textbox, Interface
 
 peft_model_id = f"alimrb/eff24"
 config = PeftConfig.from_pretrained(peft_model_id)
@@ -14,7 +18,6 @@ tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
 # Load the Lora model
 model = PeftModel.from_pretrained(model, peft_model_id)
 
-
 def make_inference(product_name, product_description):
     batch = tokenizer(
         f"### Product and Description:\n{product_name}: {product_description}\n\n### Ad:",
@@ -26,18 +29,15 @@ def make_inference(product_name, product_description):
 
     return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
 
-
 if __name__ == "__main__":
-    # make a gradio interface
-    import gradio as gr
-
-    gr.Interface(
+    # Create a Gradio interface
+    Interface(
         make_inference,
         [
-            gr.inputs.Textbox(lines=2, label="Product Name"),
-            gr.inputs.Textbox(lines=5, label="Product Description"),
+            Textbox(lines=2, label="Product Name"),
+            Textbox(lines=5, label="Product Description"),
         ],
-        gr.outputs.Textbox(label="Ad"),
+        Textbox(label="Ad"),
         title="EFF24",
-        description="EFF24 is a generative model that generates ads for products.",
-    ).launch()
+        description="EFF24 is a generative model that generates ads for products."
+    ).launch()