Create pipeline.py

pipeline.py

@@ -0,0 +1,17 @@
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+from googletrans import Translator
+from transformers import pipeline
+
+class GOTSummarizer:
+    def __init__(self):
+        self.model = T5ForConditionalGeneration.from_pretrained("iamomtiwari/NLP-Model")
+        self.tokenizer = T5Tokenizer.from_pretrained("iamomtiwari/NLP-Model")
+        self.translator = Translator()
+    
+    def summarize(self, text, max_length=150):
+        inputs = self.tokenizer("summarize: " + text, return_tensors="pt", truncation=True)
+        outputs = self.model.generate(**inputs, max_length=max_length)
+        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    def translate(self, text, lang='hi'):
+        return self.translator.translate(text, dest=lang).text