create app.py file

app.py

@@ -0,0 +1,32 @@
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+model = AutoModelForSequenceClassification.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
+tokenizer = AutoTokenizer.from_pretrained("savasy/bert-base-turkish-sentiment-cased")
+sa= pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
+
+def adjust(x):
+  if x<0:
+    return 2*x+1
+  return 2*x-1
+
+def sa2(s):
+  res= sa(s)
+  return [adjust(-1*r['score']) if r['label']=='negative' else adjust(r['score']) for r in res ]
+  
+  
+import pandas as pd
+
+import matplotlib.pyplot as plt
+def grfunc(comments):
+  df=pd.DataFrame()
+  c2=[s for s in comments.split("\n") if len(s.split())>2]
+  df["scores"]= sa2(c2)
+  df.plot(kind='hist')
+  return plt.gcf()
+ 
+import gradio as gr
+
+iface = gr.Interface(
+  fn=grfunc, 
+  inputs=gr.inputs.Textbox(placeholder="put your sentences line by line", lines=5), 
+  outputs="plot")
+iface.launch()