Update chatbot.py

chatbot.py

@@ -26,11 +26,11 @@ from gradio_client import Client, file
 from groq import Groq
 
 # You can also use models that are commented below
-# model_id = "llava-hf/llava-interleave-qwen-0.5b-hf"
-model_id = "llava-hf/llava-interleave-qwen-7b-hf"
+model_id = "llava-hf/llava-interleave-qwen-0.5b-hf"
+# model_id = "llava-hf/llava-interleave-qwen-7b-hf"
 # model_id = "llava-hf/llava-interleave-qwen-7b-dpo-hf"
 processor = LlavaProcessor.from_pretrained(model_id)
-model = LlavaForConditionalGeneration.from_pretrained(model_id,torch_dtype=torch.float16) #,  use_flash_attention_2=True)
+model = LlavaForConditionalGeneration.from_pretrained(model_id,torch_dtype=torch.int8) #,  use_flash_attention_2=True)
 model.to("cpu")
 # Credit to merve for code of llava interleave qwen
 
@@ -64,57 +64,7 @@ examples_path = os.path.dirname(__file__)
 EXAMPLES = [
     [
         {
-            "text": "What is Friction? Explain in Detail.",
-        }
-    ],
-    [
-        {
-            "text": "Write me a Python function to generate unique passwords.",
-        }
-    ],
-    [
-        {
-            "text": "What's the latest price of Bitcoin?",
-        }
-    ],
-    [
-        {
-            "text": "Search and give me list of spaces trending on HuggingFace.",
-        }
-    ],
-    [
-        {
-            "text": "Create a Beautiful Picture of Effiel at Night.",
-        }
-    ],
-    [
-        {
-            "text": "Create image of cute cat.",
-        }
-    ],
-    [
-        {
-            "text": "What unusual happens in this video.",
-            "files": [f"{examples_path}/example_video/accident.gif"],
-        }
-    ],
-    [
-        {
-            "text": "What's name of superhero in this clip",
-            "files": [f"{examples_path}/example_video/spiderman.gif"],
-        }
-    ],
-    [
-        {
-            "text": "What's written on this paper",
-            "files": [f"{examples_path}/example_images/paper_with_text.png"],
-        }
-    ],
-    [
-        {
-            "text": "Who are they? Tell me about both of them",
-            "files": [f"{examples_path}/example_images/elon_smoking.jpg",
-                      f"{examples_path}/example_images/steve_jobs.jpg", ]
+            "text": "Should I wear pyjamas in an office meeting?",
         }
     ]
 ]
@@ -123,7 +73,7 @@ EXAMPLES = [
 BOT_AVATAR = "OpenAI_logo.png"
 
 # Perform a Google search and return the results
-@lru_cache(maxsize=128) 
+@lru_cache(maxsize=64) 
 def extract_text_from_webpage(html_content):
     """Extracts visible text from HTML content using BeautifulSoup."""
     soup = BeautifulSoup(html_content, "html.parser")
@@ -137,7 +87,7 @@ def search(query):
     term = query
     start = 0
     all_results = []
-    max_chars_per_page = 8000
+    max_chars_per_page = 4000
     with requests.Session() as session:
         resp = session.get(
             url="https://www.google.com/search",