Update app.py

app.py

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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 Hema Raikhola, 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) Kanisha,Preeti,Hema and Shaksham are the team members.They are working on a science 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','hema'), auth_message = "Check your Login details sent to your email")
+