Update app.py

app.py

@@ -24,7 +24,7 @@ def draw_all(
         1. Advanced Text Summarizer
         2. Key Word Extractor
         3. Question Answering
-        4. Text Completion
+        4. Question Generation
        
         ```
         """
@@ -40,7 +40,7 @@ with st.sidebar:
 def main():
     st.title("NLP IE Web App")
     menu = ["--Select--","Summarizer",
-            "Keyword Extractor","Question Answering","Text Completion"]
+            "Keyword Extractor","Question Answering","Question Generation"]
     choice = st.sidebar.selectbox("What task would you like to do?", menu)
     if choice=="--Select--":
         
@@ -120,20 +120,22 @@ def main():
             generated_text = '. '.join(list(map(lambda x: x.strip().capitalize(), generated_text.split('.'))))
             st.write(f" Here's your Answer :\n {generated_text}")
     
-    elif choice=="Text Completion":
-        st.subheader("Text Completion")
-        st.write(" Enter the uncomplete Text to complete it automatically using AI !")
-        text_generation = pipeline("text-generation")
-        message = st.text_area("Your Text","Enter the Text to complete")
-        
-        
-        if message !="Enter the Text to complete":
-            generator = text_generation(message)
-            s1 = json.dumps(generator[0])
-            d2 = json.loads(s1)
-            generated_text = d2['generated_text']
-            generated_text = '. '.join(list(map(lambda x: x.strip().capitalize(), generated_text.split('.'))))
-            st.write(f" Here's your Generate Text :\n   {generated_text}")
+    elif choice=="Question Generation":
+        st.subheader("Question Generation")
+        st.write(" Enter the text to get questions generated !")
+        question_generator = pipeline("question-generation",model="mrm8488/t5-base-finetuned-question-generation-ap",tokenizer="mrm8488/t5-base-finetuned-question-generation-ap"
+        text_input2 = st.text_area("Your Text","Enter the Text to complete")
+        
+        
+        if text_input2:
+        # Extract named entities from the text
+        entities = extract_entities(text_input2)
+        # Generate questions based on the text using the T5 model
+        questions = question_generator(text_input2, max_length=30)
+        # Display the generated questions
+        st.subheader("Generated questions")
+        for question in questions:
+            st.write(question["question"])
 
     #main function to run
 if __name__ == '__main__':