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import gradio as gr |
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from transformers import T5Tokenizer, T5ForConditionalGeneration |
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from transformers import pipeline |
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pipe = pipeline(model="facebook/bart-large-mnli") |
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tokenizer = T5Tokenizer.from_pretrained("./model_files") |
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model = T5ForConditionalGeneration.from_pretrained("./model_files") |
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examples = [ |
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"Saurav Kant, an alumnus of upGrad and upGrad learner switches to career in ML & Al with 90% IIIT-B's PG Program in Machine learning and Artificial salary hike Intelligence, was a Sr Systems Engineer at Infosys with almost 5 years of work experience. The program and upGrad's 360-degree career support helped him transition to a Data Scientist at Tech Mahindra with 90% salary hike. upGrad's Online Power Learning has powered 3 lakh+ careers.", |
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"Kunal Shah's credit card bill payment platform, CRED, gave users a chance to win free food from Swiggy for one year. Pranav Kaushik, a Delhi techie, bagged this reward after spending 2000 CRED coins. Users get one CRED coin per rupee of bill paid, which can be used to avail rewards from brands like Ixigo, BookMyShow, UberEats, Cult.Fit and more." |
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] |
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def greet(name): |
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inp = "Summarize:" + name |
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input_ids = tokenizer(inp, return_tensors="pt").input_ids |
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outputs = model.generate(input_ids, |
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max_length=150, |
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num_beams=2, |
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repetition_penalty=2.5, |
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length_penalty=1.0, |
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early_stopping=True) |
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fin = tokenizer.decode(outputs[0],skip_special_tokens=True, clean_up_tokenization_spaces=True) |
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res = pipe(str(fin),candidate_labels=["bearish", "neutral", "bullish"],) |
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return str(fin),str(res['labels'][0]) |
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textbox = gr.Textbox(label="Enter your article:", placeholder="Article", lines=5) |
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gr.Interface(fn=greet, inputs=textbox, outputs=["text","text"], examples=examples).launch() |
