create application file

app.py

@@ -0,0 +1,97 @@
+import streamlit as st
+from huggingface_hub import snapshot_download
+
+import os  # utility library
+
+# libraries to load the model and serve inference
+import tensorflow_text
+import tensorflow as tf
+
+
+def main():
+    st.title("Interactive demo: T5 Multitasking Demo")
+    st.write("**Demo for T5's different tasks including machine translation, \
+        text summarization, document similarity, and grammatical correctness of sentences.**")
+    load_model_cache()
+    dashboard()
+
+
+@st.cache
+def load_model_cache():
+    """Function to retrieve the model from HuggingFace Hub, load it and cache it using st.cache wrapper
+    """
+    CACHE_DIR = "hfhub_cache"  # where the library's fork would be stored once downloaded
+    if not os.path.exists(CACHE_DIR):
+        os.mkdir(CACHE_DIR)
+
+    # download the files from huggingface repo and load the model with tensorflow
+    snapshot_download(repo_id="stevekola/T5", cache_dir=CACHE_DIR)
+    saved_model_path = os.path.join(CACHE_DIR, os.listdir(CACHE_DIR)[0])
+    global model
+    model = tf.saved_model.load(saved_model_path, ["serve"])
+
+
+def dashboard():
+    task_type = st.sidebar.radio("Task Type",
+                                 [
+                                  "Translate English to French",
+                                  "Translate English to German",
+                                  "Translate English to Romanian",
+                                  "Grammatical Correctness of Sentence",
+                                  "Text Summarization",
+                                  "Document Similarity Score"
+                                 ])
+    if task_type.startswith("Document Similarity"):
+        sentence1 = st.text("The first document/sentence.",
+                            "I reside in the commercial capital city of Nigeria, which is Lagos")
+        sentence2 = st.text("The second document/sentence.",
+                            "I live in Lagos")
+        sentence = sentence1 + "---" + sentence2
+    elif task_type.startswith("Text Summarization"):
+        sentence = st.text_area("Input sentence.",
+                                "I don't care about those doing the comparison, but comparing the Ghanaian Jollof Rice to \
+                          Nigerian Jollof Rice is an insult to Nigerians")
+    else:
+        sentence = st.text_area("Input sentence.",
+                                "I am Steven and I live in Lagos, Nigeria")
+    st.write("**Output Text**")
+    st.write(predict(task_type, sentence))
+
+
+def predict(task_type, sentence):
+    """Function to parse the user inputs, run the parsed text through the
+    model and return output in a readable format.
+    params:
+        task_type   sentence representing the type of task to run on T5 model
+        sentence    sentence to get inference on
+    returns:
+        text decoded into a human-readable format.
+    """
+    task_dict = {
+        "Translate English to French": "Translate English to French",
+        "Translate English to German": "Translate English to German",
+        "Translate English to Romanian": "Translate English to Romanian",
+        "Grammatical Correctness of Sentence": "cola sentence",
+        "Text Summarization": "summarize",
+        "Document Similarity Score (separate the 2 sentences with 3 dashes `---`)": "stsb",
+    }
+    question = f"{task_dict[task_type]}: {sentence}"  # parsing the user inputs into a format recognized by T5
+    # Document Similarity takes in two sentences so it has to be parsed in a separate manner
+    if task_type.startswith("Document Similarity"):
+        sentences = sentence.split('---')
+        question = f"{task_dict[task_type]} sentence1: {sentences[0]} sentence2: {sentences[1]}"
+    return predict_fn([question])[0].decode('utf-8')
+
+
+def predict_fn(x):
+    """Function to get inferences from model on live data points.
+    params:
+        x    input text to run get output on
+    returns:
+        a numpy array representing the output
+    """
+    return model.signatures['serving_default'](tf.constant(x))['outputs'].numpy()
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,3 @@
+t5
+huggingface_hub
+streamlit