Update app.py

app.py

@@ -2,13 +2,13 @@ from transformers import AutoModelForQuestionAnswering, AutoModelForSeq2SeqLM, A
 import gradio as grad
 import ast
 
-mdl_name = "deepset/roberta-base-squad2"
-my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)
+# mdl_name = "deepset/roberta-base-squad2"
+# my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)
 
-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)
+# 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 answer_question(question,context):
     text= "{"+"'question': '"+question+"','context': '"+context+"'}"
@@ -16,6 +16,7 @@ def answer_question(question,context):
     response = my_pipeline(di)
     print('response', response)
     return response
+#grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch()
 
 
 def translate(text):
@@ -24,21 +25,34 @@ def translate(text):
     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()
 
 
-
-mdl_name = "google/pegasus-xsum"
-pegasus_tkn = PegasusTokenizer.from_pretrained(mdl_name)
-mdl = PegasusForConditionalGeneration.from_pretrained(mdl_name)
+# 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()
+    
+# 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()
+
+
+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()
 
-#grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch()
-# grad.Interface(translate, inputs=['text',], outputs='text').launch()