Update app.py

app.py

@@ -1,16 +1,73 @@
+from transformers import FlaxAutoModelForSeq2SeqLM, AutoTokenizer
 import gradio as gr
-from transformers import pipeline
-
-def predict(text, name):
-    pipe = pipeline("text2text-generation", model="flax-community/t5-recipe-generation")
-    output = pipe(text)[0]
-    print("Pipeline output:", output)  # Print the complete output dictionary
-    return output["translation_text"]
-
-demo = gr.Interface(
-    fn=predict,
-    inputs=[gr.inputs.Textbox(label="Text"), gr.inputs.Textbox(label="Name")],
-    outputs=gr.outputs.Textbox(),
-    allow_sandbox_access=False,  # Disable the sandbox access
+
+MODEL_NAME_OR_PATH = "flax-community/t5-recipe-generation"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True)
+model = FlaxAutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME_OR_PATH)
+
+prefix = "items: "
+generation_kwargs = {
+    "max_length": 512,
+    "min_length": 64,
+    "no_repeat_ngram_size": 3,
+    "do_sample": True,
+    "top_k": 60,
+    "top_p": 0.95
+}
+
+special_tokens = tokenizer.all_special_tokens
+tokens_map = {
+    "<sep>": "--",
+    "<section>": "\n"
+}
+
+def skip_special_tokens(text, special_tokens):
+    for token in special_tokens:
+        text = text.replace(token, "")
+    return text
+
+def target_postprocessing(texts, special_tokens):
+    if not isinstance(texts, list):
+        texts = [texts]
+    new_texts = []
+    for text in texts:
+        text = skip_special_tokens(text, special_tokens)
+        for k, v in tokens_map.items():
+            text = text.replace(k, v)
+        new_texts.append(text)
+    return new_texts
+
+def generation_function(texts):
+    _inputs = texts if isinstance(texts, list) else [texts]
+    inputs = [prefix + inp for inp in _inputs]
+    inputs = tokenizer(
+        inputs,
+        max_length=256,
+        padding="max_length",
+        truncation=True,
+        return_tensors="jax"
+    )
+    input_ids = inputs.input_ids
+    attention_mask = inputs.attention_mask
+    output_ids = model.generate(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        **generation_kwargs
+    )
+    generated = output_ids.sequences
+    generated_recipe = target_postprocessing(
+        tokenizer.batch_decode(generated, skip_special_tokens=False),
+        special_tokens
+    )
+    return generated_recipe
+
+iface = gr.Interface(
+    fn=generation_function,
+    inputs="text",
+    outputs="text",
+    title="Recipe Generation",
+    description="Generate a recipe based on an input text."
 )
-demo.launch()
+
+if __name__ == "__main__":
+    iface.launch()