Update app.py

app.py

@@ -1,7 +1,60 @@
-import gradio as gr
+def data_summarizer(input_eng):
+    try:
+        import requests
+        from bs4 import  BeautifulSoup
+        from googletrans import Translator
+        import warnings
+        from transformers import pipeline
+        # sentiment = pipeline("sentiment-analysis")
+        from transformers import PegasusForConditionalGeneration, AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained("google/pegasus-xsum")
+        warnings.filterwarnings("ignore")
+        # from gensim.summarization.summarizer import summarize
+        from gensim.summarization import keywords
+        from textblob import TextBlob
+        translator = Translator()
+        # from transformers import pipeline
+        # summarizer = pipeline("summarization")
+        from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+        from transformers import PegasusForConditionalGeneration, AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained("google/pegasus-xsum")
+        # tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
+        model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
+        translation=translator.translate(input_eng, dest = "en")
+        tokens = tokenizer(translation.text, truncation=True, padding="longest", return_tensors="pt")
+        # Summarize 
+        summary = model.generate(**tokens)
+        # Decode summary
+        text = tokenizer.decode(summary[0]).replace("<pad> ","").replace("</s>","")
+        # summary = summarizer(translation.text)
+        # print(summary[0]['summary_text'])
+        translator = Translator()
+        # text = summary[0]['summary_text']
+        # print(keywords(text,words = 5,lemmatize=False))
+        key = keywords(text,words = 5,lemmatize=False)
+        # print(key)
+        translator = Translator()
+        keys = translator.translate(key, dest = translator.detect(input_eng).lang)
+        # print("keywords".center(50,"-"))
+        # print(keys.text,end = " ")
+        translator = Translator()
+        out = translator.translate(text, dest = translator.detect(input_eng).lang)
+        # senti = sentiment(text)[0]['label']
+        analysis=TextBlob(text)
+        #print(analysis.polarity)
+        # print(analysis.sentiment)
+        # print(f"Sentiment: {'Positive' if analysis.polarity > 0 else 'Negative' if analysis.polarity < 0 else 'Neutral' }")
+        # return {"Output_summary :":out.text,"Keywords":keys.text.replace("\n",","),"Sentiment":f"{'Positive' if analysis.polarity > 0 else 'Negative' if analysis.polarity < 0 else 'Neutral' }"}
+        # print(translation.text)
+        # print(translation.extra_data)
+        return f"{'Positive' if analysis.polarity > 0 else 'Negative' if analysis.polarity < 0 else 'Neutral' }", keys.text.replace("\n",","), out.text.strip() 
 
-def greet(name):
-    return "Hello " + name + "!!"
+    except Exception as e:
+      raise e 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# input_eng = input()
+# data_summarizer(input_eng)
+import gradio as gr   
+interface = gr.Interface(fn=data_summarizer, 
+                         inputs=gr.inputs.Textbox(lines=20, placeholder='Past your  input text...'),outputs=['text',"text","text"])
+interface.launch(inline  = False)