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| import tensorflow as tf | |
| import gradio as gr | |
| # importing necessary libraries | |
| from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering | |
| tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad") | |
| model = TFAutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad",return_dict=False) | |
| from transformers import pipeline | |
| nlp = pipeline("question-answering", model=model, tokenizer=tokenizer) | |
| context = "My name is Kanishka, i am a data scientist and machine learning engineer." | |
| question = "what is my profession?" | |
| result = nlp(question = question, context=context) | |
| print(f"QUESTION: {question}") | |
| print(f"ANSWER: {result['answer']}") | |
| # creating the function | |
| def func(context, question): | |
| result = nlp(question = question, context=context) | |
| return result['answer'] | |
| example_1 = "(1) Kanishka,Preeti,Hema and Shaksham are the team members.They are working on a machine learning project" | |
| qst_1 = "who are the team members?" | |
| example_2 = "(2) Natural Language Processing (NLP) allows machines to break down and interpret human language. It's at the core of tools we use every day – from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools." | |
| qst_2 = "What is NLP used for?" | |
| from transformers import ViltProcessor, ViltForQuestionAnswering | |
| def getResult(query, image): | |
| # prepare image + question | |
| #image = Image.open(BytesIO(base64.b64decode(base64_encoded_image))) | |
| text = query | |
| processor = ViltProcessor.from_pretrained( | |
| "dandelin/vilt-b32-finetuned-vqa") | |
| model = ViltForQuestionAnswering.from_pretrained( | |
| "dandelin/vilt-b32-finetuned-vqa") | |
| # prepare inputs | |
| encoding = processor(image, text, return_tensors="pt") | |
| # forward pass | |
| outputs = model(**encoding) | |
| logits = outputs.logits | |
| idx = logits.argmax(-1).item() | |
| print("Predicted answer:", model.config.id2label[idx]) | |
| return model.config.id2label[idx] | |
| #for youtube video summarization | |
| from transformers import pipeline | |
| from youtube_transcript_api import YouTubeTranscriptApi | |
| import gradio as gr | |
| def summarize(Youtube_Video_Link): | |
| video_id = Youtube_Video_Link.split("=")[1] | |
| try: | |
| transcript = YouTubeTranscriptApi.get_transcript(video_id) | |
| summarizer = pipeline('summarization',model='facebook/bart-large-cnn') | |
| input_text = "" | |
| for i in transcript: | |
| input_text += ' ' + i['text'] | |
| num_iters = int(len(input_text)/1000) | |
| summarized_text = [] | |
| for i in range(0, num_iters + 1): | |
| start = 0 | |
| start = i * 1000 | |
| end = (i + 1) * 1000 | |
| print("input text \n" + input_text[start:end]) | |
| out = summarizer(input_text[start:end]) | |
| out = out[0] | |
| out = out['summary_text'] | |
| print("Summarized text\n"+out) | |
| summarized_text.append(out) | |
| output_text=' '.join(summarized_text) | |
| return output_text | |
| except: | |
| return "Some Error has occurred either with Video link passed is invalid or No Captions present for this video" | |
| title = "YouTube Live Video Summarization" | |
| examples = [("https://www.youtube.com/watch?v=zKvd1JwJ4Po"),("https://www.youtube.com/watch?v=9izcbNYmP8M"),] | |
| description = "Get YouTube Video Summarization. Just Enter the YouTube Video link below. Make sure Video has Captions and it is not very long as Model Computation time will Increase." | |
| Youtube_Video_Link = gr.Textbox("Input YouTube Link here (Note: This will take time if passed a long video)", show_label=False) | |
| App= gr.Interface(fn=summarize, inputs=Youtube_Video_Link, outputs="text", examples=examples,description=description, title=title,) | |
| ## finished youtube video summarization | |
| # creating the interface | |
| iface = gr.Interface(fn=getResult, inputs=[ | |
| "text", gr.Image(type="pil")], outputs="text") | |
| # creating the interface | |
| app = gr.Interface(fn=func, | |
| inputs = ['textbox', 'text'], | |
| outputs = gr.Textbox( lines=10), | |
| title = 'Question Answering bot', | |
| description = 'Input context and question, then get answers!', | |
| examples = [[example_1, qst_1], | |
| [example_2, qst_2]], | |
| theme = "darkhuggingface", | |
| Timeout =120, | |
| allow_flagging="manual", | |
| flagging_options=["incorrect", "ambiguous", "offensive", "other"], | |
| ).queue() | |
| # launching the app | |
| gr.TabbedInterface([iface,app,App],["Visual QA","Text QA","Video Summarization"]).launch(auth = ('user','teamwork'), auth_message = "Check your Login details sent to your email") | |