Upload 2 files

app.py

@@ -0,0 +1,51 @@
+#### Import Dependencies ####
+import gradio as gr 
+import transformers 
+from transformers import pipeline
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+
+#### Model 1 ####
+model_name = "snrspeaks/t5-one-line-summary"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+#### Model 2 ####
+summarizer = pipeline(
+    "summarization",
+    "pszemraj/long-t5-tglobal-base-16384-book-summary",
+    device=0 if torch.cuda.is_available() else -1,
+)
+
+params = {
+    "max_length": 256,
+    "min_length": 8,
+    "no_repeat_ngram_size": 3,
+    "early_stopping": True,
+    "repetition_penalty": 3.5,
+    "length_penalty": 0.3,
+    "encoder_no_repeat_ngram_size": 3,
+    "num_beams": 4,
+} # parameters for text generation out of model
+
+
+#### Run the model 1####
+def summarize(text):
+    input_ids = tokenizer.encode("summarize: " + text, return_tensors="pt", add_special_tokens=True)
+    generated_id = model.generate(input_ids=input_ids,num_beams=5,max_length=50,repetition_penalty=2.5,length_penalty=1,early_stopping=True,num_return_sequences=1)
+    pred = tokenizer.decode(generated_id[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
+
+    result = summarizer(text, **params)
+    pred2 = result[0]['summary_text']
+
+    return pred, pred2
+
+#### Display summarized text ####
+with gr.Blocks() as demo:
+  text = gr.Textbox(label="Text", lines=10, placeholder="Enter text here")
+  t1 = gr.Textbox(label="Output")
+  t2 = gr.Textbox(label="Output2")
+  btn = gr.Button("Summarise")
+  btn.click(fn=summarize, inputs=text, outputs=[t1,t2])
+
+demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+transformers
+torch
+tensorflow