Update app.py

app.py

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+import streamlit as st
+import wikipedia
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.utils import clean_wiki_text, convert_files_to_docs
+from haystack.nodes import TfidfRetriever, FARMReader
+from haystack.pipelines import ExtractiveQAPipeline
+from main import print_qa, QuestionGenerator
+
+def main():
+    # Set the Streamlit app title
+    st.title("Question Generation using Haystack and Streamlit")
+
+    # Select the input type
+    inputs = ["Input Paragraph", "Wikipedia Examples"]
+    input_type = st.selectbox("Select an input type:", inputs)
+
+    # Initialize wiki_text as an empty string
+    wiki_text = ""
+
+    # Handle different input types
+    if input_type == "Input Paragraph":
+        # Allow user to input text paragraph
+        wiki_text = st.text_area("Input paragraph:", height=200)
+
+    elif input_type == "Wikipedia Examples":
+        # Define topics for selection
+        topics = ["Deep Learning", "Machine Learning"]
+        selected_topic = st.selectbox("Select a topic:", topics)
+
+        # Retrieve Wikipedia content based on the selected topic
+        if selected_topic:
+            wiki = wikipedia.page(selected_topic)
+            wiki_text = wiki.content
+
+        # Display the retrieved Wikipedia content (optional)
+        st.text_area("Retrieved Wikipedia content:", wiki_text, height=200)
+
+    # Preprocess the input text
+    wiki_text = clean_wiki_text(wiki_text)
+
+    # Allow user to specify the number of questions to generate
+    num_questions = st.slider("Number of questions to generate:", min_value=1, max_value=20, value=5)
+
+    # Allow user to specify the model to use
+    model_options = ["deepset/roberta-base-squad2", "deepset/roberta-base-squad2-distilled", "bert-large-uncased-whole-word-masking-squad2", "deepset/flan-t5-xl-squad2"]
+    model_name = st.selectbox("Select model:", model_options)
+
+    # Button to generate questions
+    if st.button("Generate Questions"):
+        document_store = InMemoryDocumentStore()
+
+        # Convert the preprocessed text into a document
+        document = {"content": wiki_text}
+        document_store.write_documents([document])
+
+        # Initialize a TfidfRetriever
+        retriever = TfidfRetriever(document_store=document_store)
+
+        # Initialize a FARMReader with the selected model
+        reader = FARMReader(model_name_or_path=model_name, use_gpu=False)
+
+        # Initialize the question generation pipeline
+        pipe = ExtractiveQAPipeline(reader, retriever)
+
+        # Initialize the QuestionGenerator
+        qg = QuestionGenerator()
+
+        # Generate multiple-choice questions
+        qa_list = qg.generate(
+            wiki_text,
+            num_questions=num_questions,
+            answer_style='multiple_choice'
+        )
+
+        # Display the generated questions and answers
+        st.header("Generated Questions and Answers:")
+        for idx, qa in enumerate(qa_list):
+            # Display the question
+            st.write(f"Question {idx + 1}: {qa['question']}")
+
+            # Display the answer options
+            if 'answer' in qa:
+                for i, option in enumerate(qa['answer']):
+                    correct_marker = "(correct)" if option["correct"] else ""
+                    st.write(f"Option {i + 1}: {option['answer']} {correct_marker}")
+
+            # Add a separator after each question-answer pair
+            st.write("-" * 40)
+
+
+
+
+
+
+
+# Run the Streamlit app
+if __name__ == "__main__":
+    main()
+
+