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a7fb809 2dc2275 50b12d3 b27a6fd 540c34c 2dc2275 a7fb809 2dc2275 a7fb809 2dc2275 540c34c 2dc2275 d2244aa 2dc2275 a7fb809 2dc2275 a7fb809 d5986cc 2dc2275 d5986cc 2dc2275 d5986cc 2dc2275 6523ce7 2dc2275 540c34c 5322a70 2dc2275 b27a6fd 69d5ad5 2dc2275 e0d1ccb a57709b 69d5ad5 a57709b 540c34c a57709b 5322a70 e0d1ccb 5322a70 a57709b 5322a70 f4bdd36 69d5ad5 5322a70 540c34c a7fb809 5322a70 69d5ad5 2dc2275 a57709b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | 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()
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