Update the eradme

app.py

@@ -10,11 +10,9 @@ if 'model' in sys.modules:
 
 from model import miniGPT
 
-# Device setup
 DEVICE = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
 print(f"Loading model on device: {DEVICE}")
 
-# Load the trained model (with config inference from state_dict, like in model.ipynb)
 SCRIPT_DIR = Path(__file__).parent
 model_dir = SCRIPT_DIR / "trained_model_small"
 
@@ -27,7 +25,6 @@ if model_dir.exists():
     for item in model_dir.iterdir():
         print(f"  - {item.name}")
     try:
-        # Load using the fixed loader that handles config/weight mismatches
         trainer = miniGPT.load(str(model_dir), map_location=DEVICE)
         trainer = trainer.to(DEVICE)
         print("✓ Model loaded successfully!")
@@ -47,7 +44,6 @@ else:
 
 
 def generate_recipe(prompt, max_length=300, temperature=0.85, top_k=50, top_p=0.95):
-    """Generate a recipe given a prompt."""
     if trainer is None:
         if error_message:
             return f"❌ Error: Model not loaded.\n\nDetails: {error_message}"
@@ -65,16 +61,18 @@ def generate_recipe(prompt, max_length=300, temperature=0.85, top_k=50, top_p=0.
     except Exception as e:
         return f"❌ Error generating recipe: {str(e)}"
 
-
-# Gradio interface
 with gr.Blocks(title="Recipe Generator", theme=gr.themes.Soft()) as demo:
     gr.Markdown(
         """
-        # 🍳 Recipe Generator
+        # Welcome eggs 1/4 cup - stupidcookgpt
+        
+        Generate recipes using a fine-tuned miniGPT model trained on recipe data.
         
-        Generate creative recipes using a fine-tuned GPT-2 model trained on recipe data.
+        Verified to generate your next delicious meal in 20 seconds (unless it doesn't).
         
-        **Model Info:** ~54M parameters, trained on HuggingFace recipes dataset
+        **Model Info:** ~124M parameters, trained on corbt/all-recipes.
+
+        *small print*: ppl this is a friggin demo not some gordon ramsay, pls use common sense and don't sue me if you eat its egg-chocolate-broccoli surprise.
         """
     )
     
@@ -87,7 +85,7 @@ with gr.Blocks(title="Recipe Generator", theme=gr.themes.Soft()) as demo:
                 value="Recipe for chocolate ",
             )
         with gr.Column():
-            generate_btn = gr.Button("Generate Recipe 🚀", variant="primary", size="lg")
+            generate_btn = gr.Button("Tell the chef...", variant="primary", size="lg")
     
     output = gr.Textbox(label="Generated Recipe", lines=12, max_lines=20)
     
@@ -109,7 +107,7 @@ with gr.Blocks(title="Recipe Generator", theme=gr.themes.Soft()) as demo:
         outputs=output,
     )
     
-    # Example prompts
+    # example prompts
     gr.Examples(
         examples=[
             ["Recipe for chocolate ", 300, 0.85, 50, 0.95],
@@ -127,14 +125,14 @@ with gr.Blocks(title="Recipe Generator", theme=gr.themes.Soft()) as demo:
     gr.Markdown(
         """
         ### How to use:
-        1. Enter a recipe prompt (e.g., "Recipe for chocolate cake")
-        2. Adjust generation parameters if desired
-        3. Click "Generate Recipe" to create a new recipe
+        1. Enter a prompt 4 the chef
+        2. Adjust chef parameters if desired (make it gordon ramsay when Temp=2 or a chill cook with Temp=0.4)
+        3. Click "Tell the chef..." to create a new recipe
         
         ### Tips:
-        - **Temperature**: Higher values (1.0+) make the model more creative but less coherent
+        - **Temperature**: Higher values (1.0+) make the chef more creative but probably more sus.
         - **Top-K**: Limits sampling to top K most likely tokens
-        - **Top-P**: Nucleus sampling - only considers tokens with cumulative probability ≤ P
+        - **Top-P**: Only considers tokens with cumulative probability <= P
         """
     )