Update app.py

app.py

@@ -1,4 +1,6 @@
+import streamlit as st
 from transformers import T5Tokenizer, FlaxT5ForConditionalGeneration
+import numpy as np
 
 MODEL_NAME_OR_PATH = "t5-base"
 tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME_OR_PATH)
@@ -46,11 +48,21 @@ def generate_recipe(items):
         attention_mask=attention_mask,
         **generation_kwargs
     )
+    
+    # Convert output IDs to numpy array
+    output_ids = np.array(output_ids)
+
     generated_recipe = tokenizer.batch_decode(output_ids, skip_special_tokens=False)
     generated_recipe = target_postprocessing(generated_recipe, tokenizer.all_special_tokens)
     return generated_recipe[0]
 
-# Example usage
-input_items = "apple, cucumber"
-generated_recipe = generate_recipe(input_items)
-print(generated_recipe)
+def main():
+    st.title("Recipe Generation")
+
+    items = st.text_input("Enter food items separated by comma (e.g., apple, cucumber):")
+    if st.button("Generate Recipe"):
+        generated_recipe = generate_recipe(items)
+        st.write(generated_recipe)
+
+if __name__ == "__main__":
+    main()