Create app.py

app.py

@@ -0,0 +1,26 @@
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("Imran1/sentimen_analysis_yelp")
+
+model = AutoModelForSequenceClassification.from_pretrained("Imran1/sentimen_analysis_yelp")
+
+from transformers import pipeline
+Data= pipeline("text-classification", model=model, tokenizer=tokenizer,top_k=5)
+
+
+
+Label=[]
+Score=[]
+def sentiment(text):
+    data = Data(text)[0]
+    for i in range (5):
+        L=data[i]["label"]
+        S=data[i]["score"]
+        Label.append(L)
+        Score.append(S)
+    return dict(zip(Label,Score))
+
+import gradio as gr
+exmp=["the food is not good.","oh I really love this food "]
+
+gr.Interface(fn=sentiment, inputs="text", outputs="label", examples=exmp,title= "Yelp reviews").launch()