Update app.py

app.py

@@ -2,7 +2,6 @@ import json
 import gradio as gr
 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
 
 tokenizer = AutoTokenizer.from_pretrained("Ashikan/dut-recipe-generator")
@@ -14,13 +13,19 @@ def perform_model_inference(ingredients_list=None, recipe_name=None):
         for ingredient_index in range(len(ingredients_list)):
             ingredients_list[ingredient_index] = ingredients_list[ingredient_index].strip()
 
-        input_text = '{"prompt": ' + json.dumps(ingredients_list)
+        # Create a simple prompt without JSON formatting
+        input_text = "Generate a recipe with these ingredients: " + ", ".join(ingredients_list)
     elif recipe_name:
-        input_text = '{"prompt": "Generate ingredients and method for the recipe: ' + recipe_name + '"}'
+        # Simple prompt for recipe name
+        input_text = "Generate ingredients and method for the recipe: " + recipe_name
     else:
         return "Invalid input"
 
-    output = pipe(input_text, max_length=1024, temperature=0.1, do_sample=True, truncation=True)[0]["generated_text"]
+    # Limit the length of the input text to avoid long processing times
+    input_text = input_text[:512]  # Truncate if it's too long
+
+    # Use 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)