Update app.py

app.py

@@ -2,39 +2,66 @@ import gradio as gr
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# Load tokenizer and model
-model_name = "Qwen/Qwen2.5-0.5B-Instruct"  # replace with actual model name or path
+# Specify the model name from HuggingFace Hub
+model_name = "Qwen/Qwen2.5-0.5B-Instruct"  # Replace with your model if needed
+
+# Load the tokenizer.
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-if torch.cuda.is_available():
-    # Load model in half precision and move to GPU for faster inference
+# Determine whether a GPU is available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+if device == "cuda":
+    # Load the model in half precision for GPU inference
     model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
-    model.to("cuda")
+    model.to(device)
 else:
-    # Load model on CPU (full precision by default)
+    # Load the model normally on CPU
     model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# Define the text generation function
-def generate_text(prompt):
-    # Tokenize input and move to appropriate device
-    inputs = tokenizer(prompt, return_tensors="pt")
-    if torch.cuda.is_available():
-        inputs = {key: value.to("cuda") for key, value in inputs.items()}
-    # Generate text
-    output_ids = model.generate(**inputs, max_length=200, do_sample=True, temperature=0.7)
-    # Decode the generated tokens to text
-    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-    return generated_text
+def generate_recommendation(characteristics: str) -> str:
+    """
+    Generates personalized diet recommendations based on input characteristics.
+    """
+    prompt = (
+        f"Based on the following characteristics, provide personalized diet recommendations:\n\n"
+        f"{characteristics}\n\n"
+        f"Diet Recommendations:"
+    )
+    try:
+        # Prepare inputs and move them to the appropriate device
+        inputs = tokenizer(prompt, return_tensors="pt")
+        inputs = {key: value.to(device) for key, value in inputs.items()}
+        
+        # Generate text
+        output_ids = model.generate(**inputs, max_length=512, num_return_sequences=1)
+        recommendation = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+        return recommendation
+    except Exception as e:
+        return f"Error: {str(e)}"
 
-# Set up Gradio interface
+# Build the Gradio Blocks interface.
 with gr.Blocks() as demo:
-    gr.Markdown("## Text Generation Demo")  # title or description (optional)
-    user_input = gr.Textbox(label="Enter your prompt:")
-    output_text = gr.Textbox(label="Generated output:")
-    generate_btn = gr.Button("Generate")
-    # When button is clicked, call generate_text with user_input and show in output_text
-    generate_btn.click(fn=generate_text, inputs=user_input, outputs=output_text)
+    gr.Markdown("# AI Diet Recommendations")
+    gr.Markdown("Get personalized diet recommendations generated by our AI.")
+    
+    with gr.Row():
+        with gr.Column():
+            characteristics_input = gr.Textbox(
+                lines=10,
+                placeholder="Enter your characteristics here...",
+                label="Your Characteristics"
+            )
+            submit_btn = gr.Button("Submit")
+        with gr.Column():
+            result_output = gr.Textbox(label="Diet Recommendations")
+    
+    # The waiting spinner will be visible while the function executes.
+    submit_btn.click(
+        fn=generate_recommendation,
+        inputs=characteristics_input,
+        outputs=result_output,
+        show_progress=True
+    )
 
-# Launch the app (if running as a standalone script)
 if __name__ == "__main__":
     demo.launch()