Upload app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import re
+import shap
+
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    TextClassificationPipeline,
+)
+
+tokenizer = AutoTokenizer.from_pretrained("chinhon/fake_tweet_detect")
+
+model = AutoModelForSequenceClassification.from_pretrained("chinhon/fake_tweet_detect")
+
+tweet_detector = TextClassificationPipeline(model=model, tokenizer=tokenizer)
+
+# tweak the extent of text cleaning as you wish
+def clean_text(text):
+    text = re.sub(r"http\S+", "", text)
+    text = re.sub(r"\n", " ", text)
+    text = re.sub(r"\'t", " not", text)  # Change 't to 'not'
+    text = re.sub(r"(@.*?)[\s]", " ", text)  # Remove @name
+    text = re.sub(r"$\d+\W+|\b\d+\b|\W+\d+$", " ", text)  # remove digits
+    text = re.sub(r"[^\w\s\#]", "", text)  # remove special characters except hashtags
+    text = text.strip(" ")
+    text = re.sub(
+        " +", " ", text
+    ).strip()  # get rid of multiple spaces and replace with a single
+    return text
+
+def tweet_detect(text):
+    data = [clean_text(text)]
+    prediction = tweet_detector(data)
+
+    pred_label = [x.get("label") for x in prediction]
+
+    if pred_label == ["LABEL_1"]:
+        return "Fake Tweet"
+    elif pred_label == ["LABEL_0"]:
+        return "Real Tweet"
+
+#Define Gradio interface
+gradio_ui = gr.Interface(
+    fn=tweet_detect,
+    title="Detect Fake Tweets",
+    description="Enter a tweet and see if the transformer model can identify if it was written by state-backed trolls.",
+    inputs=gr.inputs.Textbox(lines=10, label="Paste tweet text here [English Only]"),
+    outputs=gr.outputs.Label(type="auto", label="Prediction"),
+    interpretation="shap",
+    enable_queue=True
+)
+
+gradio_ui.launch()