Update app.py

app.py

@@ -2,7 +2,7 @@ import torch
 from peft import PeftModel, PeftConfig
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-peft_model_id = f"alimrb/eff24_farsi"
+peft_model_id = f"alimrb/eff24"
 config = PeftConfig.from_pretrained(peft_model_id)
 model = AutoModelForCausalLM.from_pretrained(
     config.base_model_name_or_path,
@@ -14,9 +14,9 @@ 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):
+def make_inference(question, answer):
     batch = tokenizer(
-        f"### Product and Description:\n{product_name}: {product_description}\n\n### Ad:",
+        f"### Question:\n{question}\n\n### Answer:",
         return_tensors="pt",
     )
 
@@ -35,10 +35,9 @@ if __name__ == "__main__":
     gr.Interface(
         make_inference,
         [
-            gr.Textbox(lines=2, label="Product Name"),
-            gr.Textbox(lines=5, label="Product Description"),
+            gr.Textbox(lines=2, label="Question"),
         ],
-        gr.Textbox(label="Ad"),
+        gr.Textbox(label="Answer"),
         title="EFF24",
-        description="EFF24 is a generative model that generates ads for products."
+        description="EFF24 is a generative model that generates Answers for Questions."
     ).launch()