Update app.py

app.py

@@ -7,12 +7,12 @@ import pandas as pd
 # Hugging Face token from environment
 HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
 
-# Four different LLM models from USA, China, and France
+# Four different LLM models from USA and China
 MODELS = [
-    "meta-llama/Llama-3.3-70B-Instruct",              # 🇺🇸 USA (Meta)
-    "Qwen/Qwen2.5-72B-Instruct",                      # 🇨🇳 China (Alibaba)
-    "deepseek-ai/DeepSeek-R1",                        # 🇨🇳 China (DeepSeek)
-    "mistralai/Ministral-3-3B-Instruct-2512"    # 🇫🇷 France (Mistral AI)
+    "meta-llama/Llama-3.3-70B-Instruct",    # 🇺🇸 USA (Meta)
+    "google/gemma-2-9b-it",                 # 🇺🇸 USA (Google)
+    "Qwen/Qwen2.5-72B-Instruct",            # 🇨🇳 China (Alibaba)
+    "deepseek-ai/DeepSeek-R1",              # 🇨🇳 China (DeepSeek)
 ]
 
 def query_model(model_id, prompt, max_tokens=300, temperature=0.7):
@@ -69,11 +69,11 @@ with gr.Blocks(title="LLM Response Collector") as demo:
     gr.Markdown("""
     # 🤖 Multi-LLM Response Collector
     
-    Compare responses from 4 different LLMs across 3 countries:
+    Compare responses from 4 different LLMs (2 US, 2 China):
     - **Llama 3.3 70B** 🇺🇸 - Meta's latest model (USA)
+    - **Gemma 2 9B** 🇺🇸 - Google's efficient model (USA)
     - **Qwen 2.5 72B** 🇨🇳 - Alibaba's flagship model (China)
     - **DeepSeek R1** 🇨🇳 - DeepSeek's reasoning model (China)
-    - **Mistral Large 3 675B** 🇫🇷 - Mistral AI's massive model (France)
     
     Each query is independent with no conversation history.
     """)