Update app.py

app.py

@@ -1,210 +1,26 @@
-from transformers import AutoModelForQuestionAnswering, AutoModelForSeq2SeqLM, AutoTokenizer, PegasusForConditionalGeneration, PegasusTokenizer, pipeline
-import gradio as grad
-import ast
+import gradio as gr
+from transformers import pipeline
+import numpy as np
 
-# mdl_name = "deepset/roberta-base-squad2"
-# my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)
+transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
 
-# model_translate_name = 'danhsf/m2m100_418M-finetuned-kde4-en-to-pt_BR'
-# model_translate = AutoModelForSeq2SeqLM.from_pretrained(model_translate_name)
-# model_translate_token = AutoTokenizer.from_pretrained(model_translate_name)
-# translate_pipeline = pipeline('translation', model=model_translate_name)
+def transcribe(stream, new_chunk):
+    sr, y = new_chunk
+    y = y.astype(np.float32)
+    y /= np.max(np.abs(y))
 
-def answer_question(question,context):
-    text= "{"+"'question': '"+question+"','context': '"+context+"'}"
-    di=ast.literal_eval(text)
-    response = my_pipeline(di)
-    print('response', response)
-    return response
-#grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch()
+    if stream is not None:
+        stream = np.concatenate([stream, y])
+    else:
+        stream = y
+    return stream, transcriber({"sampling_rate": sr, "raw": stream})["text"]
 
 
-def translate(text):
-    inputs = model_translate_token(text, return_tensor='pt')
-    translate_output = model_translate.generate(**inputs)
-    response = model_translate_token(translate_output[0], skip_special_tokens=True)
-    #response = translate_pipeline(text)
-    return response
-# grad.Interface(translate, inputs=['text',], outputs='text').launch()
+demo = gr.Interface(
+    transcribe,
+    ["state", gr.Audio(source="microphone", streaming=True)],
+    ["state", "text"],
+    live=True,
+)
 
-
-# mdl_name = "google/pegasus-xsum"
-# pegasus_tkn = PegasusTokenizer.from_pretrained(mdl_name)
-# mdl = PegasusForConditionalGeneration.from_pretrained(mdl_name)
-
-def summarize(text):
-    tokens = pegasus_tkn(text, truncation=True, padding="longest", return_tensors="pt")
-    txt_summary = mdl.generate(**tokens, num_return_sequences=5, max_length=200, temperature=1.5,num_beams=10)
-    response = pegasus_tkn.batch_decode(txt_summary, skip_special_tokens=True)
-    return response
-    
-# txt=grad.Textbox(lines=10, label="English", placeholder="English Text here")
-# out=grad.Textbox(lines=10, label="Summary")
-# grad.Interface(summarize, inputs=txt, outputs=out).launch()
-
-# ZeroShotClassification using pipeline
-# from transformers import pipeline
-# import gradio as grad
-# zero_shot_classifier = pipeline("zero-shot-classification")
-def classify(text,labels):
-    classifer_labels = labels.split(",")
-    #["software", "politics", "love", "movies", "emergency", "advertisment","sports"]
-    response = zero_shot_classifier(text,classifer_labels)
-    return response
-# txt=grad.Textbox(lines=1, label="English", placeholder="text to be classified")
-# labels=grad.Textbox(lines=1, label="Labels", placeholder="comma separated labels")
-# out=grad.Textbox(lines=1, label="Classification")
-# grad.Interface(classify, inputs=[txt,labels], outputs=out).launch()
-
-# Text classification using BartForSequenceClassification
-# from transformers import BartForSequenceClassification, BartTokenizer
-# import gradio as grad
-# bart_tkn = BartTokenizer.from_pretrained('facebook/bart-large-mnli')
-# mdl = BartForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
-def classify(text,label):
-    tkn_ids = bart_tkn.encode(text, label, return_tensors='pt')
-    tkn_lgts = mdl(tkn_ids)[0]
-    entail_contra_tkn_lgts = tkn_lgts[:,[0,2]]
-    probab = entail_contra_tkn_lgts.softmax(dim=1)
-    response =  probab[:,1].item() * 100
-    return response
-# txt=grad.Textbox(lines=1, label="English", placeholder="text to be classified")
-# labels=grad.Textbox(lines=1, label="Label", placeholder="Input a Label")
-# out=grad.Textbox(lines=1, label="Probablity of label being true is")
-# grad.Interface(classify, inputs=[txt,labels], outputs=out).launch()
-
-# GPT2
-# from transformers import GPT2LMHeadModel,GPT2Tokenizer
-# import gradio as grad
-# mdl = GPT2LMHeadModel.from_pretrained('gpt2')
-# gpt2_tkn=GPT2Tokenizer.from_pretrained('gpt2')
-def generate(starting_text):
-    tkn_ids = gpt2_tkn.encode(starting_text, return_tensors = 'pt')
-    gpt2_tensors = mdl.generate(tkn_ids,max_length=100,no_repeat_ngram_size=True,num_beams=3,do_sample=True)
-    response=""
-    #response = gpt2_tensors
-    for i, x in enumerate(gpt2_tensors):
-       response=response+f"{i}: {gpt2_tkn.decode(x, skip_special_tokens=True)}"
-    return response
-# txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
-# out=grad.Textbox(lines=1, label="Generated Text")
-# grad.Interface(generate, inputs=txt, outputs=out).launch()
-
-#DistlGPT2
-# from transformers import pipeline, set_seed
-# import gradio as grad
-# gpt2_pipe = pipeline('text-generation', model='distilgpt2')
-# set_seed(42)
-def generateDistlGPT2(starting_text):
-    response= gpt2_pipe(starting_text, max_length=20, num_return_sequences=5)
-    return response
-# txt=grad.Textbox(lines=1, label="English", placeholder="English Text here")
-# out=grad.Textbox(lines=1, label="Generated Text")
-# grad.Interface(generateDistlGPT2, inputs=txt, outputs=out).launch()
-
-#Text Generation
-#Question Generation
-# from transformers import AutoModelWithLMHead, AutoTokenizer
-# import gradio as grad
-# text2text_tkn = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
-# mdl = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
-def text2text(context,answer):
-    input_text = "answer: %s  context: %s </s>" % (answer, context)
-    features = text2text_tkn ([input_text], return_tensors='pt')
-    output = mdl.generate(input_ids=features['input_ids'],
-               attention_mask=features['attention_mask'],
-               max_length=64)
-    response=text2text_tkn.decode(output[0])
-    return response
-# context=grad.Textbox(lines=10, label="English", placeholder="Context")
-# ans=grad.Textbox(lines=1, label="Answer")
-# out=grad.Textbox(lines=1, label="Genereated Question")
-# grad.Interface(text2text, inputs=[context,ans], outputs=out).launch()
-
-#T5 summaryzer
-# from transformers import AutoTokenizer, AutoModelWithLMHead
-# import gradio as grad
-# text2text_tkn = AutoTokenizer.from_pretrained("deep-learning-analytics/wikihow-t5-small")
-# mdl = AutoModelWithLMHead.from_pretrained("deep-learning-analytics/wikihow-t5-small")
-def text2text_summary(para):
-    initial_txt = para.strip().replace("\n","")
-    tkn_text = text2text_tkn.encode(initial_txt, return_tensors="pt")
-    tkn_ids = mdl.generate(
-            tkn_text,
-            max_length=250,
-            num_beams=5,
-            repetition_penalty=2.5,
-            early_stopping=True
-        )
-    response = text2text_tkn.decode(tkn_ids[0], skip_special_tokens=True)
-    return response
-# para=grad.Textbox(lines=10, label="Paragraph", placeholder="Copy paragraph")
-# out=grad.Textbox(lines=1, label="Summary")
-# grad.Interface(text2text_summary, inputs=para, outputs=out).launch()
-
-# T5 Translate
-# from transformers import T5ForConditionalGeneration, T5Tokenizer
-# import gradio as grad
-# text2text_tkn= T5Tokenizer.from_pretrained("t5-small")
-# mdl = T5ForConditionalGeneration.from_pretrained("t5-small")
-def text2text_translation(text):
-     inp = "translate English to Portuguese: "+text
-     enc = text2text_tkn(inp, return_tensors="pt", max_length=512, truncation=True)
-     tokens = mdl.generate(**enc, max_length=100, num_return_sequences=1, early_stopping=True)
-     response=text2text_tkn.decode(tokens[0], skip_special_tokens=True)
-     return response
-# para=grad.Textbox(lines=1, label="English Text", placeholder="Text in English")
-# out=grad.Textbox(lines=1, label="Portuguese Translation")
-# grad.Interface(text2text_translation, inputs=para, outputs=out).launch()
-
-
-
-# ChatBot
-from transformers import AutoModelForCausalLM, AutoTokenizer,BlenderbotForConditionalGeneration
-import torch
-chat_tkn = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-mdl = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
-#chat_tkn = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
-#mdl = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
-
-
-def converse(user_input, chat_history=[]):
-    user_input_ids = chat_tkn(user_input + chat_tkn.eos_token, return_tensors='pt').input_ids
-    # keep history in the tensor
-    bot_input_ids = torch.cat([torch.LongTensor(chat_history), user_input_ids], dim=-1)
-    # get response
-    chat_history = mdl.generate(bot_input_ids, max_length=1000, pad_token_id=chat_tkn.eos_token_id).tolist()
-    print (chat_history)
-    response = chat_tkn.decode(chat_history[0]).split("<|endoftext|>")
-    print("starting to print response")
-    print(response)
-    # html for display
-    html = "<div class='mychat'>"
-    for x, mesg in enumerate(response):
-        if x%2!=0 :
-           mesg="Alicia:"+mesg
-           clazz="alicia"
-        else :
-           clazz="user"
-        print("value of x")
-        print(x)
-        print("message")
-        print (mesg)
-        html += "<div class='mesg {}'> {}</div>".format(clazz, mesg)
-    html += "</div>"
-    print(html)
-    return html, chat_history
-import gradio as grad
-css = """
-.mychat {display:flex;flex-direction:column}
-.mesg {padding:5px;margin-bottom:5px;border-radius:5px;width:75%}
-.mesg.user {background-color:lightblue;color:white}
-.mesg.alicia {background-color:orange;color:white,align-self:self-end}
-.footer {display:none !important}
-"""
-text=grad.inputs.Textbox(placeholder="Lets chat")
-grad.Interface(fn=converse,
-             theme="default",
-             inputs=[text, "state"],
-             outputs=["html", "state"],
-             css=css).launch()
+demo.launch()