Create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr 
+from transformers import pipeline
+
+from transformers import AutoModelForSequenceClassification,AutoTokenizer,pipeline
+model = AutoModelForSequenceClassification.from_pretrained('uer/roberta-base-finetuned-jd-binary-chinese',local_files_only=True)
+tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-finetuned-jd-binary-chinese',local_files_only=True)
+sentiment_classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+examples=["小红正在吃一块美味的蛋糕。","小红在蛋糕里发现了一只苍蝇。"]
+
+def classifier(text):
+    pred = sentiment_classifier(text)
+    print('pred=',pred)
+    pred_out = []
+    if pred[0]['label'][0:4] == 'posi':
+        dict_nega = { 'label' : '消极',     'score':1 -  pred[0]['score'], }
+        dict_posi = {'label':'积极', 'score':pred[0]['score'],}
+        pred_out.append(dict_nega)
+        pred_out.append(dict_posi)
+    else:
+        dict_nega = {'label':'消极', 'score':pred[0]['score'],}
+        dict_posi = {'label':'积极', 'score':1-pred[0]['score'],}
+        pred_out.append(dict_nega)
+        pred_out.append(dict_posi)
+    return {p["label"]: p["score"] for p in pred_out}
+
+demo = gr.Interface(classifier,
+             gr.Textbox(label="Input Text"), 
+             gr.Label(label="Predicted Sentiment"),
+             examples=examples)
+
+demo.launch()