Update app.py

app.py

@@ -33,12 +33,17 @@ def load_model_and_tokenizer(model_identifier: str, model_key: str, tokenizer_ke
 
     print(f"Loading {model_key} model ({model_identifier})...")
     try:
-        tokenizer = AutoTokenizer.from_pretrained(model_identifier, trust_remote_code=True)
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_identifier, 
+            trust_remote_code=True,
+            use_auth_token=False  # Ensure we're not using auth for public models
+        )
         model = AutoModelForCausalLM.from_pretrained(
             model_identifier,
             torch_dtype=torch.bfloat16,
             device_map="auto",
             trust_remote_code=True,
+            use_auth_token=False  # Ensure we're not using auth for public models
         )
         model.eval()
 
@@ -53,6 +58,9 @@ def load_model_and_tokenizer(model_identifier: str, model_key: str, tokenizer_ke
         return model, tokenizer
     except Exception as e:
         print(f"ERROR loading {model_key} model ({model_identifier}): {e}")
+        print(f"Error type: {type(e).__name__}")
+        if "404" in str(e) or "not found" in str(e).lower():
+            print(f"Model {model_identifier} not found. Please check the model ID.")
         _models_cache[model_key] = "error"
         _models_cache[tokenizer_key] = "error"
         raise
@@ -173,12 +181,12 @@ with gr.Blocks(theme=gr.themes.Soft(), title="🎬 CineGuide Comparison") as dem
     gr.Markdown(
         f"""
         # 🎬 CineGuide vs. Base Model Comparison
-        Compare the fine-tuned CineGuide movie recommender with the base {BASE_MODEL_ID.split('/')[-1]} model.
+        Compare your fine-tuned CineGuide movie recommender with the base {BASE_MODEL_ID.split('/')[-1]} model.
         
-        **Base Model:** `{BASE_MODEL_ID}`  
-        **Fine-tuned Model:** `{FINETUNED_MODEL_ID}`
+        **Base Model:** `{BASE_MODEL_ID}` (Standard Assistant)
+        **Fine-tuned Model:** `{FINETUNED_MODEL_ID}` (CineGuide - Specialized for Movies)
         
-        Type your movie-related query below and see how each model responds!
+        Type your movie-related query below and see how fine-tuning improves movie recommendations!
         
         ⚠️ **Note:** Models are loaded on first use and may take 30-60 seconds initially.
         """
@@ -205,7 +213,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="🎬 CineGuide Comparison") as dem
             
         with gr.Column(scale=1):
             gr.Markdown(f"## 🎬 CineGuide (Fine-tuned)")
-            gr.Markdown(f"*Specialized for movie recommendations*")
+            gr.Markdown(f"*Specialized movie recommendation model*")
             chatbot_ft = gr.ChatInterface(
                 respond_ft,
                 textbox=gr.Textbox(placeholder="Ask CineGuide about movies...", container=False, scale=7),