Create app.py

app.py

@@ -0,0 +1,47 @@
+import matplotlib.pyplot as plt
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+import tweepy
+
+
+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 ]
+   
+def get_examples():
+  #return [e for e in  open("examplesTR.csv").readlines()]
+  return [["#demokrasi","100","","","",""]]
+
+def grfunc(key, number_of_tweets,consumer_key, consumer_secret,acc_token,acc_secret):
+  auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
+  auth.set_access_token(acc_token, acc_secret)
+  api = tweepy.API(auth)
+  msgs = []
+  for tweet in tweepy.Cursor(api.search, q=key, lang='tr', rpp=100).items(50):
+    msgs.append(tweet.text)
+  c2=[s.strip() for s in msgs 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 #hashtag"),
+          gr.inputs.Textbox(placeholder="the number of tweets",default=100),
+          gr.inputs.Textbox(placeholder="consumer_key"),
+          gr.inputs.Textbox(placeholder="consumer_secret"),
+          gr.inputs.Textbox(placeholder="access_key"),
+          gr.inputs.Textbox(placeholder="access_secret")
+          ], 
+  outputs="plot",
+  examples=get_examples())
+iface.launch()