Delete chatbots.py

import os
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
import random
from langchain import HuggingFaceHub
from langchain.callbacks import get_openai_callback


def main():
# ---------------------------- created personal API -----------------------------
os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"

# ------------------ Designing Page ---------------
st.set_page_config(page_title="Ask Your PDF")
st.header("Ask your PDF :")

pdf = st.file_uploader("Upload your File here", type="pdf")

# Check Pdf
if pdf is not None:
pdf_reader = PdfReader(pdf)

text = ""

# Extract pages from pdf
for page in pdf_reader.pages:
text += page.extract_text()

# split into chunks
text_spliter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len
)

chunks = text_spliter.split_text(text)

# create embeddings
embedding = HuggingFaceEmbeddings()
knowledge_base = FAISS.from_texts(chunks, embedding)

user_questions = st.text_input("Ask a Question from PDF : ")
if user_questions:

greeting = ["hy", 'hello', 'hey', "hi"]
greet_msg = ["Hello Dear!", 'Hey!', 'Hey Friend!']
if user_questions in greeting:
response = random.choice(greet_msg)
elif user_questions == "by" or user_questions == "bye":
response = "GoodBye Sir!, Have a Nice Day....."
else:
docs = knowledge_base.similarity_search(user_questions)
chain = load_qa_chain(
HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 1, "max_length": 512}),
chain_type="stuff")
with get_openai_callback() as cb:
response = chain.run(input_documents=docs, question=user_questions)
print(cb)
st.write(response)


if __name__ == "__main__":
main()

chatbots.py

@@ -1,63 +0,0 @@
-import os
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chains.question_answering import load_qa_chain
-import random
-from langchain import HuggingFaceHub
-from langchain.callbacks import get_openai_callback
-
-def main():
-    # ---------------------------- created personal API -----------------------------
-    os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"
-
-    # ------------------ Designing Page ---------------
-    st.set_page_config(page_title="Ask Your PDF")
-    st.header("Ask your PDF :")
-
-    pdf = st.file_uploader("Upload your File here", type="pdf")
-
-    # Check Pdf
-    if pdf is not None:
-        pdf_reader = PdfReader(pdf)
-
-        text = ""
-
-        # Extract pages from pdf
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-
-        # split into chunks
-        text_spliter = CharacterTextSplitter(
-            separator="\n",
-            chunk_size=1000,
-            chunk_overlap=200,
-            length_function=len
-        )
-
-        chunks = text_spliter.split_text(text)
-
-        # create embeddings
-        embedding = HuggingFaceEmbeddings()
-        knowledge_base = FAISS.from_texts(chunks, embedding)
-
-        user_questions = st.text_input("Ask a Question from PDF : ")
-        if user_questions:
-
-            greeting = ["hy", 'hello', 'hey', "hi"]
-            greet_msg = ["Hello Dear!", 'Hey!', 'Hey Friend!']
-            if user_questions in greeting:
-                response = random.choice(greet_msg)
-            elif user_questions == "by" or user_questions == "bye":
-                response = "GoodBye Sir!, Have a Nice Day....."
-            else:
-                docs = knowledge_base.similarity_search(user_questions)
-                chain = load_qa_chain(HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.9, "max_length":512}), chain_type="stuff")
-                with get_openai_callback() as cb:
-                    response = chain.run(input_documents=docs, question=user_questions)
-                    print(cb)
-            st.write(response)
-if __name__ == "__main__":
-    main()