Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load a model from Hugging Face for recipe generation
+model = pipeline("text2text-generation", model="flax-community/t5-recipe-generation")
+
+# Recipe generation function
+def suggest_recipes(ingredients):
+    prompt = f"You are an expert in cooking. Please suggest 3 recipes using the following ingredients: {ingredients}. Provide a title for each recipe, include preparation time, and list step-by-step directions."
+    response = model(prompt, max_length=512, num_return_sequences=3)
+    
+    # Parse model output into a structured format
+    recipes = []
+    for i, recipe in enumerate(response):
+        text = recipe["generated_text"]
+        
+        # Split into meaningful sections for formatting
+        lines = text.split("\n")
+        title = f"Recipe {i+1}: {lines[0]}" if lines else f"Recipe {i+1}: Title Missing"
+        prep_time = next((line for line in lines if "Preparation time" in line), "Preparation time: Not provided")
+        directions = "\n".join([f"{idx+1}. {line}" for idx, line in enumerate(lines[1:]) if line])
+        
+        recipes.append(f"{title}\n{prep_time}\nDirections:\n{directions}\n\nReady to serve!")
+    
+    return "\n\n".join(recipes)
+
+# Gradio interface
+with gr.Blocks() as app:
+    gr.Markdown("# Recipe Suggestion App")
+    gr.Markdown("Provide the ingredients you have, and this app will suggest recipes along with preparation times!")
+    
+    with gr.Row():
+        ingredients_input = gr.Textbox(label="Enter Ingredients (comma-separated):", placeholder="e.g., eggs, milk, flour")
+    
+    recipe_output = gr.Textbox(label="Suggested Recipes:", lines=15, interactive=False)
+    
+    generate_button = gr.Button("Get Recipes")
+    
+    generate_button.click(suggest_recipes, inputs=ingredients_input, outputs=recipe_output)
+
+# Launch the app
+app.launch()