Create app.py

app.py

@@ -0,0 +1,28 @@
+import gradio as gr
+from transformers import pipeline
+
+# pipe = pipeline(task="text2text-generation", model="Jumpy-pku/t5-dish-name-recognition")
+# pipe_aux = pipeline(task="text2text-generation", model="Jumpy-pku/t5-dish-name-recognition-auxiliary")
+pipe = pipeline(task="text2text-generation", model="model/t5-name-cot")
+pipe_aux = pipeline(task="text2text-generation", model="model/t5-component")
+
+def predict(input_text):
+    
+    verbs = pipe_aux(f"“{input_text}”这个菜谱的主要操作是什么？", max_length=20, num_return_sequences=1)[0]["generated_text"]
+    flavs = pipe_aux(f"“{input_text}”这个菜谱的主要风味是什么？", max_length=20, num_return_sequences=1)[0]["generated_text"]
+    ings = pipe_aux(f"“{input_text}”这个菜谱的主要食材是什么？", max_length=20, num_return_sequences=1)[0]["generated_text"]
+
+    comp_prompt = ""
+    if verbs:
+        comp_prompt += f"这个菜谱的主要操作是{verbs},"
+    if flavs:
+        comp_prompt += f"这个菜谱的主要风味是{flavs},"
+    if ings:
+        comp_prompt += f"这个菜谱的主要食材是{ings},"
+    comp_prompt += "这个菜谱的菜名是什么？"
+
+    return pipe(f"“{input_text}”{comp_prompt}", max_length=20, num_return_sequences=1)[0]["generated_text"]
+
+    
+demo = gr.Interface(fn=predict, inputs="text", outputs="text", title="菜名生成", description="输入菜谱步骤，生成菜名。")
+demo.launch()