Update app.py

app.py

@@ -1,124 +1,98 @@
 import json
 import gradio as gr
+import spaces
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
 from format.format_output import format_output
+from validate.validate_ingredients import validate_ingredients
 from device.get_device_id import get_device_id
 
+# Load the model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("Ashikan/dut-recipe-generator")
 model = AutoModelForCausalLM.from_pretrained("Ashikan/dut-recipe-generator")
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=get_device_id())
 
+@spaces.GPU
 def perform_model_inference(ingredients_list=None, recipe_name=None):
-    try:
-        if ingredients_list:
-            for ingredient_index in range(len(ingredients_list)):
-                ingredients_list[ingredient_index] = ingredients_list[ingredient_index].strip()
-
-            # Create a simple prompt for generating a recipe from ingredients
-            input_text = "Generate a recipe with these ingredients: " + ", ".join(ingredients_list)
-        elif recipe_name:
-            # Simple prompt for generating ingredients and method from recipe name
-            input_text = "Generate ingredients and cooking method for the recipe: " + recipe_name
-        else:
-            return "Invalid input"
-
-        # Limit the length of the input text to avoid long processing times
-        input_text = input_text[:512]  # Truncate if it's too long
-
-        # Generate output with a higher temperature for quicker responses
-        output = pipe(input_text, max_length=512, temperature=0.7, do_sample=True, truncation=True)[0]["generated_text"]
-
-        return format_output(output)
+    # If ingredients are provided
+    if ingredients_list:
+        for ingredient_index in range(len(ingredients_list)):
+            ingredients_list[ingredient_index] = ingredients_list[ingredient_index].strip()
+        input_text = '{"prompt": ' + json.dumps(ingredients_list)
     
-    except Exception as e:
-        return f"Error occurred: {str(e)}"
-
-def chat_function(history, user_input, mode):
-    # If mode is "ingredients", process as ingredient list
-    if mode == "ingredients":
-        ingredients_list = user_input.lower().split(',')
-        
-        # Validate the ingredients
-        if len(ingredients_list) < 2:
-            error_text = "Please provide at least 2 ingredients, separated by commas."
-            history.append((user_input, error_text))
-            return history, ""
-
-        # Generate the recipe
-        history.append((user_input, "Generating recipe..."))
-        recipe = perform_model_inference(ingredients_list=ingredients_list)
-        history[-1] = (user_input, recipe)  # Replace the "Generating recipe..." message with the result
-        return history, ""
-
-    # If mode is "recipe", process as recipe name
-    elif mode == "recipe":
-        recipe_name = user_input.strip()
-
-        # Validate the recipe name
-        if not recipe_name:
-            error_text = "Please provide a valid recipe name."
-            history.append((user_input, error_text))
-            return history, ""
-
-        # Generate ingredients and method
-        history.append((user_input, "Generating ingredients and method..."))
-        recipe_details = perform_model_inference(recipe_name=recipe_name)
-        history[-1] = (user_input, recipe_details)  # Replace the "Generating ingredients..." message with the result
-        return history, ""
-
-# Define the Gradio interface
-with gr.Blocks(css=""" 
-#chatbot-container {
-    background-color: #f7f7f8;
-    border-radius: 10px;
-    padding: 10px;
-    max-width: 800px;
-    margin: auto;
-    box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1);
-}
-.chat-message {
-    border-radius: 8px;
-    padding: 10px;
-    margin-bottom: 10px;
-    font-family: Arial, sans-serif;
-}
-.user-message {
-    background-color: #e6f7ff;
-    text-align: left;
-}
-.bot-message {
-    background-color: #f0f0f0;
-    text-align: left;
-}
-#chatbot-container .user-message {
-    color: #0073e6;
-}
-#chatbot-container .bot-message {
-    color: #333;
-}
-.gr-button {
-    background-color: #0073e6 !important;
-    color: white !important;
-    border: none !important;
-    border-radius: 5px !important;
-}
-""") as recipegen:
-    gr.Markdown("# Recipe Generator")
-    gr.Markdown("An AI model attempting to produce healthier, diabetic-friendly recipes. Start by entering ingredients or a recipe name.")
-
-    chatbot = gr.Chatbot(elem_id="chatbot-container")
-    user_input = gr.Textbox(placeholder="Enter ingredients or recipe name", label="Your Input")
+    # If recipe name is provided
+    elif recipe_name:
+        input_text = "Generate ingredients and cooking method for the recipe: " + recipe_name
+    else:
+        return "Invalid input"
     
-    # Dropdown for selecting input mode (ingredients or recipe name)
-    mode_selector = gr.Dropdown(
-        choices=["ingredients", "recipe"], 
-        label="Select Input Mode", 
-        value="ingredients"
+    # Generate output from the model
+    output = pipe(input_text, max_length=1024, temperature=0.1, do_sample=True, truncation=True)[0]["generated_text"]
+    return format_output(output)
+
+def generate_recipe(ingredients=None, recipe_name=None):
+    # If ingredients are provided
+    if ingredients:
+        ingredients_list = ingredients.lower().split(',')
+        is_ingredients_valid = validate_ingredients(ingredients_list)
+
+        if is_ingredients_valid:
+            generated_text = perform_model_inference(ingredients_list=ingredients_list)
+            return {
+                generated_recipe: gr.Markdown(value=generated_text, label="Generated Recipe",
+                                              elem_id="recipe-container", visible=True)
+            }
+        else:
+            error_text = "## Invalid ingredients. Please include at least 2 ingredients in a comma-separated list. e.g. brown rice, onions, garlic"
+            return {
+                generated_recipe: gr.Markdown(value=error_text, elem_id="recipe-container", visible=True)
+            }
+
+    # If recipe name is provided
+    if recipe_name:
+        generated_text = perform_model_inference(recipe_name=recipe_name)
+        return {
+            generated_recipe: gr.Markdown(value=generated_text, label="Generated Recipe",
+                                          elem_id="recipe-container", visible=True)
+        }
+
+    return {
+        generated_recipe: gr.Markdown(value="## Please enter either ingredients or a recipe name.", 
+                                      elem_id="recipe-container", visible=True)
+    }
+
+# Gradio Interface
+with gr.Blocks(css="./css/styles.css") as recipegen:
+    gr.Image("./assets/dut.png", interactive=False, show_share_button=False, show_download_button=False,
+             show_fullscreen_button=False, show_label=False, elem_id="dut-logo", height=256)
+    gr.Markdown("#  Recipe Generator", elem_id="header")
+    gr.Markdown("### An AI Model Attempting To Produce Healthier, Diabetic-Friendly Recipes",
+                elem_id="header-sub-heading")
+    gr.Markdown("Start by entering a comma-separated list of ingredients or a recipe name below.", elem_id="header-instructions")
+
+    with gr.Column() as column:
+        user_ingredients = gr.Textbox(label="Ingredients", placeholder="e.g. chicken, onion, garlic", autofocus=True, max_lines=1, elem_id="ingredients-input")
+        user_recipe_name = gr.Textbox(label="Recipe Name", placeholder="e.g. Chicken Biryani", max_lines=1, elem_id="recipe-name-input")
+        generate_button = gr.Button(value="Generate")
+        
+    with gr.Column():
+        generated_recipe = gr.Markdown(visible=True)
+
+    examples = gr.Examples(
+        elem_id="examples",
+        examples=[
+            "sweet potato, mushrooms, cheese, garlic",
+            "chicken breast, chili, onion, tomato, parmesan cheese",
+            "strawberries, vanilla, honey, rolled oats, almonds, butter",
+            "hake, spring onion, lemon"
+        ],
+        inputs=[user_ingredients]
     )
 
-    submit_button = gr.Button("Generate")
-
-    # Link the chatbot and input to the chat function
-    submit_button.click(chat_function, inputs=[chatbot, user_input, mode_selector], outputs=[chatbot, user_input])
+    generate_button.click(
+        fn=generate_recipe,
+        inputs=[user_ingredients, user_recipe_name],
+        outputs=[generated_recipe]
+    )
 
 recipegen.launch(share=True)