created app.py

app.py

@@ -0,0 +1,28 @@
+import tensorflow as tf
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+import gradio as gr
+import numpy as np
+from scipy.special import softmax
+from sklearn import metrics
+import matplotlib.pyplot as plt
+from scipy.interpolate import interp1d
+import pandas as pd
+
+tokenizer = AutoTokenizer.from_pretrained('roberta-base')
+
+from transformers import AutoModel
+model = AutoModel.from_pretrained("ilan541/OncUponTim")
+
+def predict(your_text):
+  df = split_text(text)
+  inp = tokenizer(your_text, return_tensors='tf')
+  if np.argmax(softmax(model(inp).logits)) == 0:
+    return 'This content is not of high standard. It needs editing. '
+  else:
+    return 'Promising content! Our algorithm predicts it will be very popular.'
+
+
+    ##### add possibility to play with the threshold
+
+iface = gr.Interface(fn=predict, inputs="text", outputs="text")
+iface.launch()