Delete streamlit.py

streamlit.py

@@ -1,100 +0,0 @@
-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()
-
-