Update app.py

app.py

@@ -2,13 +2,27 @@ import gradio as gr
 from transformers import pipeline
 
 # Load a model from Hugging Face for recipe generation
-model = pipeline("text-generation", model="flax-community/t5-recipe-generation")           
+model = pipeline("text-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)
-    return response
-  
+    prompt = (
+        f"You are an expert in cooking. Please suggest 3 recipes using the following "
+        f"ingredients: {ingredients}. Provide a title for each recipe, include "
+        f"preparation time, and list step-by-step directions."
+    )
+    response = model(prompt, max_length=512, num_return_sequences=1)
+    
+    # Parse the generated text to create structured recipes
+    generated_text = response[0]['generated_text']
+    recipes = generated_text.split("Recipe ")
+    structured_recipes = []
+    
+    for i, recipe in enumerate(recipes):
+        if recipe.strip():  # Ensure non-empty recipe
+            structured_recipes.append(f"Recipe {i+1}:\n{recipe.strip()}")
+    
+    return "\n\n".join(structured_recipes)
 
 # Gradio interface
 with gr.Blocks() as app: