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| import streamlit as st | |
| from langchain.vectorstores import FAISS | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.schema import Document | |
| from transformers import pipeline | |
| import pickle | |
| import time | |
| from string import Template | |
| def load_faiss_index(index_file="faiss_course_index.pkl"): | |
| with open(index_file, "rb") as f: | |
| return pickle.load(f) | |
| def get_faiss_retriever(index_file="faiss_course_index.pkl"): | |
| faiss_index = load_faiss_index(index_file) | |
| documents = [Document(page_content=item['description'], metadata=item) for item in faiss_index['metadata']] | |
| retriever = FAISS.from_documents(documents, HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")).as_retriever() | |
| return retriever | |
| def get_answer_from_t5(query, context): | |
| t5_pipeline = pipeline("text2text-generation", model="t5-small", device=0) | |
| input_text = f"Question: {query} Context: {context}" | |
| answer = t5_pipeline(input_text, max_length = 500,truncation = True) | |
| return answer[0]['generated_text'] | |
| def search_and_answer(query, index_file="faiss_course_index.pkl"): | |
| retriever = get_faiss_retriever(index_file=index_file) | |
| retrieved_docs = retriever.get_relevant_documents(query) | |
| if not retrieved_docs: | |
| return "No relevant courses found." | |
| count = len(retrieved_docs) | |
| print(f"Related Courses: {count}") | |
| course_info = [ | |
| (doc.metadata.get('title', 'No Title'), doc.metadata.get('lessons', 'No lessons listed')) | |
| for doc in retrieved_docs | |
| ] | |
| response = "\nRelated Courses:\n" | |
| for course_title, course_lessons in course_info: | |
| response += f"Course Title: {course_title}\nCourse Lessons: {course_lessons}\n\n" | |
| context = retrieved_docs[0].page_content | |
| summary = get_answer_from_t5(query, context) | |
| response += f"Summary: {summary}" | |
| return response, course_info, summary | |
| def load_card_template(): | |
| with open("card_template.html",'r') as file: | |
| return file.read() | |
| def main(): | |
| st.title("📚 Course Search WebApp") | |
| query = st.text_input("🔍 Enter your query:") | |
| if st.button("Search"): | |
| with st.spinner("Fetching results..."): | |
| start_time = time.time() | |
| response, course_info, summary = search_and_answer(query, index_file="faiss_course_index.pkl") | |
| if response == "No relevant courses found.": | |
| st.error(response) | |
| else: | |
| st.success("✅ Results fetched successfully!") | |
| st.write(f"⏱ **Time Taken:** {time.time() - start_time:.2f} seconds") | |
| # Load the card template HTML | |
| card_template = load_card_template() | |
| # Display related courses as cards in two columns | |
| st.subheader("🎓 Related Courses:") | |
| st.write("<div style='display: flex; flex-wrap: wrap; justify-content: space-between;'>", unsafe_allow_html=True) | |
| for course_title, course_lessons in course_info: | |
| card_html = Template(card_template).substitute(course_title=course_title, course_lessons=course_lessons) | |
| st.write(card_html, unsafe_allow_html=True) | |
| st.write("</div>", unsafe_allow_html=True) | |
| # Display summary with better layout | |
| st.subheader("📝 Summary:") | |
| st.info(summary[:500]) | |
| st.markdown("<p style='text-align: center;'>Developed with ❤️ by Prachi</p>", unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| main() | |