Added all categories back to demo.

Gemma.py

@@ -1,4 +1,3 @@
-# from transformers import AutoTokenizer, Gemma3ForCausalLM
 from transformers import pipeline
 from huggingface_hub import login
 import spaces
@@ -15,31 +14,10 @@ class GemmaLLM:
         
         model_id = "google/gemma-3-4b-it"
         
-        # self.tokenizer = AutoTokenizer.from_pretrained(model_id)
-        # self.model = Gemma3ForCausalLM.from_pretrained(
-        #     model_id,
-        #     device_map="cuda" if torch.cuda.is_available() else "cpu",
-        #     torch_dtype=torch.float16,
-        # ).eval()
-        
         self.model = pipeline("text-generation", model=model_id, torch_dtype=torch.bfloat16, device="cuda")
         
     @spaces.GPU
     def generate(self, message) -> str:
-        # inputs = self.tokenizer.apply_chat_template(
-        #     message,
-        #     add_generation_prompt=True,
-        #     tokenize=True,
-        #     return_dict=True,
-        #     return_tensors="pt",
-        # ).to(self.model.device)
-
-        # input_length = inputs["input_ids"].shape[1]
-
-        # with torch.inference_mode():
-        #     outputs = self.model.generate(**inputs, max_new_tokens=1024)[0][input_length:]
-        #     outputs = self.tokenizer.decode(outputs, skip_special_tokens=True)
-        
         outputs = self.model(message, max_new_tokens=1024)[0]["generated_text"]
         
         return outputs

News.py

@@ -8,10 +8,10 @@ class News:
     __EX_SOURCES__ = ["ABC News", "Bloomberg", "The Hill", "Fox Sports", "Google News", "Newsweek", "Politico"]
     __CATEGORIES__ = [
         "General",
-        # "Business",
-        # "Entertainment",
-        # "Health",
-        # "Science",
+        "Business",
+        "Entertainment",
+        "Health",
+        "Science",
         "Technology"
     ]