PDF-Summarizer-and-ChatPDF

app.py

@@ -1,59 +0,0 @@
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.chat_models import ChatOpenAI
-from langchain.callbacks import get_openai_callback
-
-api_key = "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"  # your API key
-
-def main():
-    st.set_page_config(page_title="PDF Summarizer")
-    st.header("PDF Summarizer")
-
-    pdf = st.file_uploader("Upload PDF file here", type="pdf")
-
-    if pdf is not None:
-        pdf_reader = PdfReader(pdf)
-        text = ""
-        
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-
-        text_splitter = CharacterTextSplitter(
-            separator="\n",
-            chunk_size=1000,
-            chunk_overlap=200,
-            length_function=len             
-        )
-
-        chunks = text_splitter.split_text(text)
-
-        embeddings = OpenAIEmbeddings(
-            openai_api_key=api_key,
-            openai_api_base="https://openrouter.ai/api/v1"
-        )
-
-        knowledge_base = FAISS.from_texts(chunks, embeddings)
-        user_question = st.text_input("Ask Question to uploaded PDF!")
-
-        if user_question:
-            docs = knowledge_base.similarity_search(user_question)
-
-            llm = ChatOpenAI(
-                openai_api_key=api_key,
-                openai_api_base="https://openrouter.ai/api/v1",
-                model_name="gpt-4o-mini"  # ✅ must be model_name
-            )
-
-            chain = load_qa_chain(llm, chain_type="stuff")
-
-            with get_openai_callback() as cb:
-                response = chain.run(input_documents=docs, question=user_question)
-                print(cb)
-
-            st.write(response)
-
-main()

pdfchat.py

@@ -1,59 +0,0 @@
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.chat_models import ChatOpenAI
-from langchain.callbacks import get_openai_callback
-
-api_key = "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"  # your API key
-
-def main():
-    st.set_page_config(page_title="PDF Summarizer")
-    st.header("PDF Summarizer")
-
-    pdf = st.file_uploader("Upload PDF file here", type="pdf")
-
-    if pdf is not None:
-        pdf_reader = PdfReader(pdf)
-        text = ""
-        
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-
-        text_splitter = CharacterTextSplitter(
-            separator="\n",
-            chunk_size=1000,
-            chunk_overlap=200,
-            length_function=len             
-        )
-
-        chunks = text_splitter.split_text(text)
-
-        embeddings = OpenAIEmbeddings(
-            openai_api_key=api_key,
-            openai_api_base="https://openrouter.ai/api/v1"
-        )
-
-        knowledge_base = FAISS.from_texts(chunks, embeddings)
-        user_question = st.text_input("Ask Question to uploaded PDF!")
-
-        if user_question:
-            docs = knowledge_base.similarity_search(user_question)
-
-            llm = ChatOpenAI(
-                openai_api_key=api_key,
-                openai_api_base="https://openrouter.ai/api/v1",
-                model_name="gpt-4o-mini"  # ✅ must be model_name
-            )
-
-            chain = load_qa_chain(llm, chain_type="stuff")
-
-            with get_openai_callback() as cb:
-                response = chain.run(input_documents=docs, question=user_question)
-                print(cb)
-
-            st.write(response)
-
-main()