Update app.py

app.py

@@ -12,8 +12,8 @@ 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()
+    model = load_model_cache()
+    dashboard(model)
 
 
 @st.cache
@@ -27,11 +27,11 @@ def load_model_cache():
     # 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"])
+    return model
+    
 
-
-def dashboard():
+def dashboard(model):
     task_type = st.sidebar.radio("Task Type",
                                  [
                                   "Translate English to French",
@@ -55,10 +55,10 @@ def dashboard():
         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))
+    st.write(predict(task_type, sentence, model))
 
 
-def predict(task_type, sentence):
+def predict(task_type, sentence, model):
     """Function to parse the user inputs, run the parsed text through the
     model and return output in a readable format.
     params:
@@ -73,7 +73,7 @@ def predict(task_type, sentence):
         "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",
+        "Document Similarity Score": "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
@@ -83,7 +83,7 @@ def predict(task_type, sentence):
     return predict_fn([question])[0].decode('utf-8')
 
 
-def predict_fn(x):
+def predict_fn(x, model):
     """Function to get inferences from model on live data points.
     params:
         x    input text to run get output on