Create app.py

app.py

@@ -0,0 +1,54 @@
+from dotenv import load_dotenv
+import os
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS #facebook AI similarity search
+from langchain.chains.question_answering import load_qa_chain
+from langchain import HuggingFaceHub
+
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Ask your PDF")
+    st.header("Ask Your PDF")
+
+    pdf = st.file_uploader("Upload your pdf",type="pdf")
+
+    if pdf is not None:
+        pdf_reader = PdfReader(pdf)
+        text = ""
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+
+        # spilit ito chuncks
+        text_splitter = CharacterTextSplitter(
+            separator="\n",
+            chunk_size=1000,
+            chunk_overlap=200,
+            length_function=len
+        )
+        chunks = text_splitter.split_text(text)
+
+        # create embedding
+        embeddings = HuggingFaceEmbeddings()
+
+        knowledge_base = FAISS.from_texts(chunks,embeddings)
+
+        user_question = st.text_input("Ask Question about your PDF:")
+        if user_question:
+            docs = knowledge_base.similarity_search(user_question)
+            llm = HuggingFaceHub(repo_id="google/flan-t5-large", model_kwargs={"temperature":5,
+                                                      "max_length":64})
+            chain = load_qa_chain(llm,chain_type="stuff")
+            response = chain.run(input_documents=docs,question=user_question)
+
+            st.write(response)
+
+
+
+        # st.write(chunks)
+
+if __name__ == '__main__':
+    main()