Update app.py

app.py

@@ -1,27 +1,49 @@
+import requests
 import gradio as gr
 import preprocessor as tweet_cleaner
-import requests
-from transformers import pipeline
+# from transformers import pipeline
+
+# pretrained_name = "w11wo/indonesian-roberta-base-sentiment-classifier"
+
+# sentiment = pipeline(
+#     "sentiment-analysis",
+#     model=pretrained_name,
+#     tokenizer=pretrained_name,
+#     max_length=512,
+#     truncation=True,
+# )
 
-pretrained_name = "w11wo/indonesian-roberta-base-sentiment-classifier"
+API_URL = "https://api-inference.huggingface.co/models/w11wo/indonesian-roberta-base-sentiment-classifier"
+headers = {"Authorization": "Bearer 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"}
+
+def format_sentiment(predictions):
+  formatted_output = dict()
+
+  for p in predictions:
+    if p['label'] == 'positive':
+      formatted_output['Positif'] = p['score']
+    elif p['label'] == 'negative':
+      formatted_output['Negatif'] = p['score']
+    else:
+      formatted_output['Netral'] = p['score']
+  
+  return formatted_output
 
-sentiment = pipeline(
-    "sentiment-analysis",
-    model=pretrained_name,
-    tokenizer=pretrained_name,
-    max_length=512,
-    truncation=True,
-)
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.json()
 
 def clean_tweet(tweet):
     return tweet_cleaner.clean(tweet)
 
 def get_sentiment(input_text):
-  return sentiment(clean_tweet(input_text))
+  res = query({"inputs": clean_tweet(input_text)})
+  formatted_output = format_sentiment(res[0])
+  return formatted_output
   
 iface = gr.Interface(fn = get_sentiment, 
                      inputs = 'text', 
-                     outputs = ['text'], 
+                     outputs = ['label'], 
                      title = 'Analisis Sentimen Twitter', 
                      description="Dapatkan sentimen postiif, negatif, atau netral untuk tweet yang dimasukkan.")