Update app.py

app.py

@@ -1,23 +1,19 @@
 import streamlit as st
-from llama_cpp import Llama
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# Initialize the Llama model
-llm = Llama.from_pretrained(
-    repo_id="RichardErkhov/mrm8488_-_gpt2-finetuned-recipes-cooking-gguf",
-    filename="gpt2-finetuned-recipes-cooking.IQ3_M.gguf",
-)
+# Initialize model and tokenizer
+model_name = "RichardErkhov/mrm8488_-_gpt2-finetuned-recipes-cooking"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
 def generate_recipe(dish_name):
-    # Create a prompt with the user's favorite dish
-    messages = [
-        {
-            "role": "user",
-            "content": f"Provide a step-by-step recipe for {dish_name}."
-        }
-    ]
-    # Get the recipe from the model
-    response = llm.create_chat_completion(messages=messages)
-    return response['choices'][0]['message']['content']
+    # Tokenize input
+    inputs = tokenizer(f"Recipe for {dish_name}:", return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=300)
+    
+    # Decode generated text
+    recipe = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return recipe
 
 # Streamlit app
 st.title("Cooking Recipe Generator")