Update app.py

app.py

@@ -25,14 +25,6 @@ model = InstructBlipForConditionalGeneration.from_pretrained(MODEL_ID).to(device
 def answer_ad_listing_question(
     image: PIL.Image.Image,
     title: str,
-    decoding_method: str = "Nucleus sampling",
-    temperature: float = 1.0,
-    length_penalty: float = 1.0,
-    repetition_penalty: float = 1.5,
-    max_length: int = 50,
-    min_length: int = 1,
-    num_beams: int = 5,
-    top_p: float = 0.9,
 ) -> str:
     # The prompt template with the provided title
     prompt = f"""Given an ad listing with the title '{title}' and image, answer the following questions without any explanation or extra text:
@@ -46,14 +38,14 @@ def answer_ad_listing_question(
     inputs = processor(images=image, text=prompt, return_tensors="pt").to(device, torch.float16)
     generated_ids = model.generate(
         **inputs,
-        do_sample=decoding_method == "Nucleus sampling",
-        temperature=temperature,
-        length_penalty=length_penalty,
-        repetition_penalty=repetition_penalty,
-        max_length=max_length,
-        min_length=min_length,
-        num_beams=num_beams,
-        top_p=top_p,
+        do_sample=True,  # Nucleus sampling is applied
+        temperature=1.0,  # Default temperature
+        length_penalty=1.0,  # Default length penalty
+        repetition_penalty=1.5,  # Default repetition penalty
+        max_length=50,  # Default max length
+        min_length=1,  # Default min length
+        num_beams=5,  # Default number of beams
+        top_p=0.9,  # Default top_p for nucleus sampling
     )
     result = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
     return result
@@ -82,18 +74,7 @@ with gr.Blocks() as demo:
     # Logic to handle clicking on "Analyze Ad Listing"
     submit_button.click(
         fn=answer_ad_listing_question,
-        inputs=[
-            image,
-            ad_title,  # The title from the ad
-            "Nucleus sampling", # Default values for decoding method, temperature, etc.
-            1.0, # temperature
-            1.0, # length_penalty
-            1.5, # repetition_penalty
-            50,  # max_length
-            1,   # min_length
-            5,   # num_beams
-            0.9, # top_p
-        ],
+        inputs=[image, ad_title],  # Only the image and ad title are inputs
         outputs=answer_output,
     )