creating app.py for the AIrecipeWizard project.

app.py

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+from huggingface_hub import InferenceClient
+from PIL import Image
+import gradio as gr
+import os
+import json
+# Function to generate recipe from ingredients
+def generate_recipe(ingredients: str, model_name_recipe: str, token: str) -> str:
+    """
+    Generates a recipe based on input ingredients using an LLM.
+    Args:
+        ingredients (str): Ingredients input by the user.
+        model_name_recipe (str): Hugging Face model for text generation.
+        token (str): API token for Hugging Face authentication.
+    Returns:
+        str: Generated recipe text.
+    """
+    prompt = f"Generate a recipe using the following ingredients: {ingredients}. Write the recipe in detail along with a suitable title."
+    client = InferenceClient(model_name_recipe, token=token)
+    try:
+        response = client.post(
+            json={
+                "inputs": prompt,
+                "parameters": {"max_new_tokens": 500},
+                "task": "text-generation",
+            }
+        )
+        return json.loads(response.decode())[0]["generated_text"]
+    except Exception as e:
+        return f"Error generating recipe: {e}"    
+# Function to generate image of the recipe
+def generate_image(recipe_title: str, model_name_image: str, token: str) -> Image.Image:
+    """
+    Generates an image based on the recipe title using a text-to-image model.
+    Args:
+        recipe_title (str): Title of the recipe to use as the prompt.
+        model_name_image (str): Hugging Face model for image generation.
+        token (str): API token for Hugging Face authentication.
+    Returns:
+        PIL.Image.Image: Generated image.
+    """
+    client = InferenceClient(model_name_image, token=token)
+    try:
+        prompt = f"A photo the dish: {recipe_title}, showing delicious presentation."
+        return client.text_to_image(prompt)
+    except Exception as e:
+        print(f"Error generating image: {e}")
+        return None    
+# Gradio function that combines both recipe generation and image generation
+def generate_recipe_and_image(message: str, history):
+    token = os.getenv("API_Token_HF_AIrecipe")  # Ensure your Hugging Face token is set in environment variables
+    model_name_recipe = "microsoft/Phi-3-mini-4k-instruct"  # Replace with the LLM model of your choice
+    model_name_image = "prompthero/openjourney"  # Replace with the image generation model of your choice
+    
+    # Generate recipe
+    recipe_text = generate_recipe(message, model_name_recipe, token)
+    prompt_to_remove = f"Generate a recipe using the following ingredients: {message}. Write the recipe in detail along with a suitable title."
+    if prompt_to_remove in recipe_text:
+        recipe_text = recipe_text.replace(prompt_to_remove, "").strip()
+    # print("recipe_text:",recipe_text)
+    # Extract recipe title from generated recipe text
+    lines = recipe_text.split("\n")
+    for line in lines:
+        if line.lower().startswith("title:"):  # Case-insensitive match
+            recipe_title = line.replace("Title:", "").strip()  # Extract and clean title
+            break 
+    print("recipe_title",recipe_title)    
+    # Generate image for the recipe
+    recipe_image = generate_image(recipe_title, model_name_image, token)
+    image_path = "./recipe_image.png"
+    recipe_image.save(image_path, format="PNG")
+    
+    # Combine text and image
+    return recipe_text, gr.Image(value=image_path)    
+    
+
+if __name__ == "__main__":
+    # Gradio UI
+    with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+        gr_image = gr.Image(render=False)
+        with gr.Row():
+            with gr.Column(scale=4):
+                gr.Markdown("<center><h1>AI Recipe Chatbot</h1></center>")
+                chatbot = gr.ChatInterface(
+                    generate_recipe_and_image,
+                    examples=["chicken, rice, tomatoes, onions, spices", "oats, milk, fruits", "Noodles, Tofu, Soy sauce"],
+                    type="messages",
+                    additional_outputs=[gr_image]
+                )
+            with gr.Column(scale=1):
+                gr.Markdown("<center><h1>Recipe Image</h1></center>")
+                # recipe_image = gr.Image(label="Generated Recipe Image")
+                gr_image.render()  
+    demo.launch()
+
+
+
+
+
+