Create app.py

app.py

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+# import the module
+import streamlit as st
+from transformers import pipeline
+
+
+#This function accepts the masked text like: "How are [MASK]"
+# and feeds this text to the model and prints the output in which [MASK] is filled with the appropriate word.
+def print_the_mask(text):
+
+    #Import the model
+    model = pipeline(task="fill-mask",
+                     model="MUmairAB/bert-based-MaskedLM")
+
+    #Apply the model
+    model_out = model("I want to [MASK]")
+
+    #First sort the list of dictionaries according to the score
+    model_out = sorted(model_out, key=lambda x: x['score'],reverse=True)
+    for sub_dict in model_out:
+        print(sub_dict["sequence"])
+
+
+#The main function that will be executed when this file is executed
+def main():
+    # Set the title
+    st.title("Masked Language Model App")
+    st.write("Created by: [Umair Akram](https://www.linkedin.com/in/m-umair01/)")
+   
+    h1 = "This App uses a fine-tuned DistilBERT-Base-Uncased Masked Language Model to predict the missed word in a sentence."
+    st.subheader(h1)
+
+    st.write("Its code and other interesting projects are available on my [website](https://mumairab.github.io/)")
+    h2 = "Enter your text and put \"[MASK]\" at the word which you want to predict, as shown in the following example: How are [MASK]"
+    st.write(h2)
+
+    text = st.text_input(label="Enter your text here:",
+                 value="Type here ...")
+
+    if(st.button('Submit')):
+        # Perform the input validation
+        if "[MASK]" not in text:
+            st.write("You did not enter \"[MASK]\" in the text. Please write your text again!")
+        else:
+            print_the_mask(text)