Update app.py

app.py

@@ -19,7 +19,7 @@ from transformers import AutoProcessor, LlavaForConditionalGeneration
 from transformers import BitsAndBytesConfig
 import torch
 from huggingface_hub import InferenceClient
-
+import spaces 
 IS_SPACES_ZERO = os.environ.get("SPACES_ZERO_GPU", "0") == "1"
 IS_SPACE = os.environ.get("SPACE_ID", None) is not None
 
@@ -119,7 +119,6 @@ def find_nearby(place=None):
     print("The 5 closest locations are:\n")
     print(closest_hotels)
     return closest_hotels
-
 @spaces.GPU
 # Define the respond function
 def search_hotel(place=None):
@@ -142,11 +141,9 @@ def search_hotel(place=None):
             response = requests.get(image_url, verify=False)
             response.raise_for_status()
             img = Image.open(BytesIO(response.content))
-
             prompt = "USER: <image>\nAnalyze this image. Give me feedback on whether this hotel is worth visiting based on the picture. Provide a summary review.\nASSISTANT:"
             outputs = pipe_image_to_text(img, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
             description = outputs[0]["generated_text"].split("\nASSISTANT:")[-1].strip()
-
             description_data.append({'hotel_name': hotel_name, 'hotel_id': hotel_id, 'image': img, 'description': description})
         except (requests.RequestException, UnidentifiedImageError):
             print(f"Skipping image at URL: {image_url}")
@@ -253,7 +250,6 @@ def llm_results(description_df):
     conversation = [[{"text": "Based on your search...", "files": []}, {"text": f"**My recommendation:** {result}", "files": []}]]
     return conversation
 
-@spaces.GPU
 def chatbot_response(user_input, conversation):
     bot_initial_message = {
         "text": f"Looking for hotels in {user_input}...",