Update app.py

app.py

@@ -3,7 +3,11 @@ import transformers
 import numpy as np
 
 # Load the pre-trained model
-model = transformers.pipeline("text2text-generation", model="mrm8488/t5-base-finetuned-topic-modeling")
+model1 = transformers.pipeline("text2text-generation", model="bigscience/T0pp")
+model2 = transformers.pipeline("text2text-generation", model="google/flan-t5-xxl")
+model3 = transformers.pipeline("text2text-generation", model="google/flan-t5-xl")
+model4 = transformers.pipeline("text2text-generation", model="tuner007/pegasus_paraphrase")
+model5 = transformers.pipeline("text2text-generation", model="tuner007/pegasus_paraphrase")
 
 # Define the Streamlit app
 def main():
@@ -12,12 +16,20 @@ def main():
 
     if st.button("Generate Topics"):
         # Generate topics
-        topics = model(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7)
+        topics1 = model1(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7)
+        topics2 = model2(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7)
+        topics3 = model3(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7)
+        topics4 = model4(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7)
+        topics5 = model5(text, max_length=50, do_sample=True, num_beams=5, temperature=0.7)
 
         # Print topics
         st.write("Top 5 topics:")
         for i in range(5):
-            st.write(f"{i+1}. {topics[i]['generated_text']}")
+            st.write(f"{i+1}. {topics1[i]['generated_text']}")
+            st.write(f"{i+1}. {topics2[i]['generated_text']}")
+            st.write(f"{i+1}. {topics3[i]['generated_text']}")
+            st.write(f"{i+1}. {topics4[i]['generated_text']}")
+            st.write(f"{i+1}. {topics5[i]['generated_text']}")
 
 if __name__ == "__main__":
     main()