Update app.py

app.py

@@ -0,0 +1,81 @@
+import os
+import streamlit as st
+import pickle
+import time
+from langchain import OpenAI
+from langchain.chains import RetrievalQAWithSourcesChain
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import UnstructuredURLLoader
+# from langchain.embeddings import OpenAIEmbeddings
+from langchain.embeddings import FakeEmbeddings
+from langchain.llms import HuggingFaceHub
+from langchain.chains import LLMChain
+from langchain.vectorstores import FAISS
+
+from dotenv import load_dotenv
+load_dotenv()  # take environment variables from .env (especially openai api key)
+
+st.title("RockyBot: News Research Tool 📈")
+st.sidebar.title("News Article URLs")
+
+urls = []
+for i in range(3):
+    url = st.sidebar.text_input(f"URL {i+1}")
+    urls.append(url)
+
+process_url_clicked = st.sidebar.button("Process URLs")
+file_path = "faiss_store_openai.pkl"
+
+main_placeholder = st.empty()
+llm = HuggingFaceHub( repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.5, "max_length": 64} )
+
+@st.cache
+def process_urls(urls):
+    """Processes the given URLs and saves the FAISS index to a pickle file."""
+
+    # load data
+    loader = UnstructuredURLLoader(urls=urls)
+
+    # split data
+    text_splitter = RecursiveCharacterTextSplitter(
+        separators=['\n\n', '\n', '.', ','],
+        chunk_size=1000
+    )
+    docs = text_splitter.split_documents(loader.load())
+
+    # create embeddings and save it to FAISS index
+    embeddings = FakeEmbeddings(size=1352)
+    vectorstore_openai = FAISS.from_documents(docs, embeddings)
+
+    # Save the FAISS index to a pickle file
+    with open(file_path, "wb") as f:
+        pickle.dump(vectorstore_openai, f)
+
+if process_url_clicked:
+    with st.progress(0.0):
+        process_urls(urls)
+        st.progress(100.0)
+
+query = main_placeholder.text_input("Question: ")
+if query:
+    try:
+        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)
+    except Exception as e:
+        st.error(e)
+
+if __name__ == '__main__':
+    st.main()