.

app.py

@@ -1,20 +1,6 @@
-import streamlit as st
-from transformers import pipeline
-from PIL import Image
+from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
-
-st.title("Hot Dog? Or Not?")
-
-file_name = st.file_uploader("Upload a hot dog candidate image")
-
-if file_name is not None:
-    col1, col2 = st.columns(2)
-
-    image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
-    predictions = pipeline(image)
-
-    col2.header("Probabilities")
-    for p in predictions:
-        col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-cluecorpussmall")
+model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-cluecorpussmall")
+text_generator = TextGenerationPipeline(model, tokenizer)
+text_generator("这是很久之前的事情了", max_length=100, do_sample=True)