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Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["gunicorn", "-b","0.0.0.0:7860" "main:app"]

main.py

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+from flask import Flask,render_template,url_for,request
+from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification
+
+# name = 'Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset'
+
+# tokenizer = AutoTokenizer.from_pretrained(name)
+# model = AutoModelForSequenceClassification.from_pretrained('/Users/jorgemeneumoreno/Desktop/FakeNewsClassifier/model',  max_position_embeddings=512)
+
+#pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer)
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+pipe = pipeline("text-classification", model="Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset")
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+tokenizer = AutoTokenizer.from_pretrained("Giyaseddin/distilbert-base-cased-finetuned-fake-and-real-news-dataset")
+model = AutoModelForSequenceClassification.from_pretrained('model', max_position_embeddings=512)
+
+application = app = Flask(__name__)
+
+@application.route('/')
+def home():
+	return render_template('home.html')
+@application.route('/predict',methods=['POST'])
+
+def predict():
+    if request.method == 'POST':
+        input_message = request.form['message']
+        if len(input_message)>=511:
+             input_message= input_message[0:512]
+        if input_message.strip() == "":
+            result="Please enter the body of an article"
+        my_input = [input_message]
+        preds = pipe(my_input, return_all_scores=True)
+        output_dict = {'Real': preds[0][0]['score'], 'Fake': preds[0][1]['score']}
+        print(output_dict)
+        print(list(output_dict.keys()), list(output_dict.values()))
+        props = [(round(float(v)*100, 2)) for v in list(output_dict.values())]
+        print(props)
+        return render_template('result.html', mess = input_message, classes = list(output_dict.keys()), props=props)
+
+if __name__ == '__main__':
+    app.run(port=5000,debug=True)

requirements.txt

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+flask
+transformers
+gunicorn