Create app.py

app.py

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+import os
+import gradio
+from PIL import Image
+from timeit import default_timer as timer
+from tensorflow import keras
+import torch
+from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM, AutoModelForSequenceClassification, create_optimizer, DataCollatorForSeq2Seq
+import numpy as np
+
+loaded_model = AutoModelForSequenceClassification.from_pretrained("runaksh/financial_summary_T5_base")
+loaded_tokenizer = AutoTokenizer.from_pretrained("runaksh/financial_summary_T5_base")
+
+# Function for generating summary
+def generate_summary(text,min_length=55,max_length=80):
+  text = "summarize: "+text
+  input = tokenizer(text,max_length=512,truncation=True,return_tensors="tf").input_ids
+  op=model.generate(input,min_length=min_length,max_length=max_length)
+  decoded_op = tokenizer.batch_decode(op,skip_special_tokens=True)
+  return decoded_op
+
+title = "Financial News Summary"
+description = "Enter the news"
+
+# Gradio elements
+
+# Input from user
+in_prompt = gradio.components.Textbox(lines=2, label='Enter the News')
+
+# Output response
+out_response = gradio.components.Textbox(label='Summary')
+
+# Gradio interface to generate UI link
+iface = gradio.Interface(fn=generate_summary,
+                         inputs = in_prompt,
+                         outputs = out_response,
+                         title=title,
+                         description=description
+                         )
+
+iface.launch(debug = True)