Init

app.py

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+import re
+import gradio as gr
+from transformers import pipeline
+from transformers import AutoTokenizer
+from transformers import AutoModelForSequenceClassification
+
+def process_tweet(tweet):
+    # remove links
+    tweet = re.sub('((www\.[\s]+)|(https?://[^\s]+))', '', tweet)
+    # remove usernames
+    tweet = re.sub('@[^\s]+', '', tweet)
+    # remove additional white spaces
+    tweet = re.sub('[\s]+', ' ', tweet)
+    # replace hashtags with words
+    tweet = re.sub(r'#([^\s]+)', r'\1', tweet)
+    # trim
+    tweet = tweet.strip('\'"')
+    return tweet #if len(tweet) > 0 else ""
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "azamat/geocoder_model_xlm_roberta_50"
+)
+
+relevancy_pipeline = pipeline("sentiment-analysis", model="azamat/geocoder_model")
+
+coordinates_model = AutoModelForSequenceClassification.from_pretrained(
+    "azamat/geocoder_model_xlm_roberta_50",
+)
+
+def predict_relevancy(text):
+    outputs = relevancy_pipeline(text)
+    return outputs[0]['label'], outputs[0]['score']
+
+def predict_coordinates(text):
+    encoding = tokenizer(text, padding="max_length", truncation=True, \
+        max_length=128, return_tensors='pt')
+    outputs = coordinates_model(**encoding)
+    return outputs[0][0], outputs[0][1]
+
+def predict(text):
+    text = process_tweet(text)
+    relevancy_label, relevancy_score = predict_relevancy(text)
+    if relevancy_label == 'relevant':
+        lat, lon = predict_coordinates(text)
+        return f"Relevancy model is confident for {relevancy_score * 100}% that tweet has the geolocation relevant information.\n" + \
+            f"Precited location coordinates are: lat: {lat} lon: {lon}"
+    return f"Relevancy model is confident for {relevancy_score * 100}% that tweet does not have the geolocation relevant information."
+
+iface = gr.Interface(fn=predict, inputs="text", outputs="text")
+iface.launch()

requirements.txt

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+transformers 
+datasets