Initial app.py

app.py

@@ -1,20 +1,25 @@
-import gradio as gr
-from transformers import BertTokenizer, BartForConditionalGeneration
-
-
-tokenizer = BertTokenizer.from_pretrained("HIT-TMG/dialogue-bart-large-chinese")
-model = BartForConditionalGeneration.from_pretrained("HIT-TMG/dialogue-bart-large-chinese")
-
+# import gradio as gr
+# from transformers import BertTokenizer, BartForConditionalGeneration
+#
+#
+# tokenizer = BertTokenizer.from_pretrained("HIT-TMG/dialogue-bart-large-chinese")
+# model = BartForConditionalGeneration.from_pretrained("HIT-TMG/dialogue-bart-large-chinese")
+#
+#
+# def generate_reponse(history):
+#     history_prefix = "对话历史："
+#     history = history_prefix + history
+#
+#     input_ids = tokenizer(history, return_tensors='pt').input_ids
+#     output_ids = model.generate(input_ids)[0]
+#
+#     return tokenizer.decode(output_ids, skip_special_tokens=True)
+#
+#
+# iface = gr.Interface(fn=generate_reponse, inputs="text", outputs="text")
+# iface.launch()
 
-def generate_reponse(history):
-    history_prefix = "对话历史："
-    history = history_prefix + history
-
-    input_ids = tokenizer(history, return_tensors='pt').input_ids
-    output_ids = model.generate(input_ids)[0]
-
-    return tokenizer.decode(output_ids, skip_special_tokens=True)
 
+import gradio as gr
 
-iface = gr.Interface(fn=generate_reponse, inputs="text", outputs="text")
-iface.launch()
+gr.Interface.load("models/facebook/bart-large-cnn").launch()