created topic.py

topics.py

@@ -0,0 +1,39 @@
+import streamlit as st
+
+def load_topic_transfomers():
+  from transformers import pipeline
+  try:
+      topic_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli",device="cuda", compute_type="float16")
+  except Exception as e:
+      topic_classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+      print("Error: ", e)
+  return topic_classifier
+
+def suggest_topic(text):
+
+    while len(text)> 1024:
+        text = summarize(whole_text[:-10])
+
+    possible_topics = ["Gadgets", 'Business','Finance', 'Health', 'Sports',  'Politics','Government','Science','Education', 'Travel', 'Tourism', 'Finance & Economics','Market','Technology','Scientific Discovery',
+                      'Entertainment','Environment','News & Media' "Space,Universe & Cosmos", "Fashion", "Manufacturing and Constructions","Law & Crime","Motivation", "Development & Socialization",  "Archeology"]
+
+    result = topic_classifier(text, possible_topics)
+
+    return result['labels']
+
+st.title("Topic Suggestion")
+with st.spinner(Loading Model):
+topic_classifier = load_topic_transfomers()
+  st.success(Model_loaded)
+
+whole_text = st.text_input("Enter the text Here: ")
+predicted_topic = suggest_topic(whole_text)
+
+st.write('Suggested Topics')
+for i in predicted_topic:
+  st.write(i)
+
+
+
+
+