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e1e283c 47752b1 e1e283c 47752b1 | 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 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | import os
import streamlit as st
import pickle
import time
import requests
from bs4 import BeautifulSoup
from langchain.llms import HuggingFacePipeline
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.docstore.document import Document
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from dotenv import load_dotenv
load_dotenv()
st.title("Research Summarizer and Question Answering Tool 📈")
st.sidebar.title("Article URLs")
# Add option to choose input method
input_method = st.sidebar.radio("Input Method:", ["URLs", "Paste Text"])
# Initialize Hugging Face models
@st.cache_resource
def initialize_hf_models():
# Initialize tokenizer and model for text generation (using FLAN-T5 which is free and good for Q&A)
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
# Create pipeline
pipe = pipeline(
"text2text-generation",
model=model,
tokenizer=tokenizer,
max_length=500,
temperature=0.9,
)
# Create LangChain wrapper
llm = HuggingFacePipeline(pipeline=pipe)
return llm
llm = initialize_hf_models()
# Input fields based on selected method
if input_method == "URLs":
urls = []
for i in range(3):
url = st.sidebar.text_input(f"URL {i+1}")
urls.append(url)
process_button_label = "Process URLs"
else: # Paste Text
st.sidebar.info("💡 Paste article text below (useful when websites block scraping)")
pasted_texts = []
for i in range(3):
text = st.sidebar.text_area(f"Article {i+1} Text:", height=100, key=f"text_{i}")
if text.strip():
pasted_texts.append(text)
process_button_label = "Process Texts"
process_url_clicked = st.sidebar.button(process_button_label)
file_path = "faiss_store_hf.pkl"
main_placeholder = st.empty()
def extract_text_from_url(url):
try:
# Enhanced headers to mimic a real browser more closely
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
'Accept-Language': 'en-US,en;q=0.5',
'Accept-Encoding': 'gzip, deflate, br',
'DNT': '1',
'Connection': 'keep-alive',
'Upgrade-Insecure-Requests': '1',
'Sec-Fetch-Dest': 'document',
'Sec-Fetch-Mode': 'navigate',
'Sec-Fetch-Site': 'none',
'Cache-Control': 'max-age=0',
}
# Add a delay to avoid rate limiting (increased for stricter sites)
time.sleep(2)
response = requests.get(url, headers=headers, timeout=15, allow_redirects=True)
response.raise_for_status()
soup = BeautifulSoup(response.text, 'html.parser')
# Remove unwanted elements
for tag in soup(['script', 'style', 'nav', 'header', 'footer', 'ads']):
tag.decompose()
# Extract text from paragraphs
paragraphs = soup.find_all('p')
text = ' '.join([p.get_text().strip() for p in paragraphs if p.get_text().strip()])
return text
except Exception as e:
st.error(f"Error processing {url}: {str(e)}")
return None
if process_url_clicked:
documents = []
if input_method == "URLs":
# Validate URLs
valid_urls = [url for url in urls if url.strip() != ""]
if not valid_urls:
st.error("Please enter at least one valid URL")
st.stop()
try:
# load data from URLs
main_placeholder.text("Data Loading...Started...✅✅✅")
for url in valid_urls:
text = extract_text_from_url(url)
if text:
doc = Document(page_content=text, metadata={"source": url})
documents.append(doc)
if not documents:
st.error("Could not fetch content from any of the URLs. Please check if the URLs are accessible.")
st.info("💡 TIP: If websites are blocking access, try using 'Paste Text' method instead!")
st.stop()
except Exception as e:
st.error(f"An error occurred: {str(e)}")
st.stop()
else: # Paste Text method
if not pasted_texts:
st.error("Please paste at least one article text")
st.stop()
try:
# load data from pasted text
main_placeholder.text("Processing Pasted Text...Started...✅✅✅")
for idx, text in enumerate(pasted_texts):
doc = Document(page_content=text, metadata={"source": f"Pasted Article {idx+1}"})
documents.append(doc)
except Exception as e:
st.error(f"An error occurred: {str(e)}")
st.stop()
# Continue with text splitting (same for both methods)
if documents:
# split data
text_splitter = RecursiveCharacterTextSplitter(
separators=['\n\n', '\n', '.', ','],
chunk_size=1000
)
main_placeholder.text("Text Splitter...Started...✅✅✅")
docs = text_splitter.split_documents(documents)
if not docs:
st.error("No text content could be extracted.")
st.stop()
# create embeddings and save it to FAISS index
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
vectorstore = FAISS.from_documents(docs, embeddings)
main_placeholder.text("Embedding Vector Started Building...✅✅✅")
time.sleep(2)
# Save the FAISS index to a pickle file
with open(file_path, "wb") as f:
pickle.dump(vectorstore, f)
query = main_placeholder.text_input("Question: ")
if query:
if os.path.exists(file_path):
with open(file_path, "rb") as f:
vectorstore = pickle.load(f)
chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vectorstore.as_retriever())
result = chain({"question": query}, return_only_outputs=True)
# result will be a dictionary of this format --> {"answer": "", "sources": [] }
st.header("Answer")
st.write(result["answer"])
# Display sources, if available
sources = result.get("sources", "")
if sources:
st.subheader("Sources:")
sources_list = sources.split("\n") # Split the sources by newline
for source in sources_list:
st.write(source)
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