Create app.py

app.py

@@ -0,0 +1,66 @@
+import streamlit as st
+from dotenv import load_dotenv
+from pypdf import PdfReader
+from langchain.text_splitter import CharacterTextSplitter as CSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.memory import ConversationalBufferMemory()
+
+def get_pdf_text(docs):
+    text = ""
+    for pdf in docs:
+        reader = PdfReader(pdf)
+        for page in reader.pages:
+            text += page.extract_text()
+
+def get_text_chunks(text):
+    splitter = CSplitter(
+        separator="\n",
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
+    )
+    chunks = splitter.split_text(text)
+    return chunks
+
+def get_embeddings():
+    model_name = "sentence-transformers/all-mpnet-base-v2"
+    model_kwargs = {'device': 'cpu'}
+    encode_kwargs = {'normalize_embeddings': False}
+    
+    return HuggingFaceEmbeddings( 
+        model_name=model_name,
+        model_kwargs=model_kwargs,
+        encode_kwargs=encode_kwargs
+    )
+
+def get_vectorstore(chunks):
+    hf = get_embeddings()
+    vectorstore = FAISS.from_texts(text=chunks, embedding=hf)
+    conversation = get_conversation_chain(vectorstore)
+
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="IDSR Chat", page_icon=":books:")
+    st.header("IntelSurv Chat")
+    st.text_input("Ask a question")
+
+    with st.sidebar:
+        st.subheader("TG for IDSR Booklet")
+        docs= st.file_uploader("Upload booklet here", accept_multiple_files=True)
+        
+        if st.button("Process"):
+            with st.spinner("Processing"):
+                raw_text = get_pdf_text(docs)
+
+                chunks = get_text_chunks()
+        st.write(chunks)
+
+
+
+
+
+
+if __name__ == '__main__':
+    main()