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main.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+from langchain_community.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+from langchain_openai import OpenAI, ChatOpenAI
+from langchain_openai import OpenAIEmbeddings
+
+load_dotenv()
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            try:
+                page_text = page.extract_text()
+                if page_text:
+                    text += page_text
+            except Exception as e:
+                print(f"Error reading page: {e}")
+    return text
+
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2500, chunk_overlap=750)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+def get_vector_store(text_chunks):
+    vector_store = FAISS.from_texts(text_chunks, OpenAIEmbeddings())
+    vector_store.save_local("faiss_index")
+
+def get_conversational_chain():
+    prompt_template = """You are an assistant for teachers. Your objective is to provide
+    comprehensive and accurate responses based on the context provided. Make sure that 
+    you generate whole output.
+    context: {context}
+    question: {question}
+    """
+
+    model = ChatOpenAI(model="gpt-3.5-turbo")
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
+    return chain
+
+def user_input(user_question):
+    embeddings = OpenAIEmbeddings()
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    docs = new_db.similarity_search(user_question)
+
+    chain = get_conversational_chain()
+    result = ""
+    with st.spinner("Processing..."):
+        response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
+        result = response["output_text"]
+    
+    st.session_state.chat_history.append({"role": "assistant", "content": result})
+
+def main():
+    st.set_page_config("Chat PDF")
+    st.header("Lesson Plan Maker With AI💁")
+
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = []
+
+    with st.sidebar:
+#        st.logo("pic123.png")
+        st.image("pic123.png")
+        st.title("Menu:")
+        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
+        if st.button("Submit & Process"):
+            with st.spinner("Processing..."):
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks)
+                st.success("Done")
+
+    # Display chat history
+    for idx, chat in enumerate(st.session_state.chat_history):
+        with st.chat_message(chat["role"]):
+            st.write(chat["content"])
+            if chat["role"] == "assistant":
+                st.download_button(
+                    label="Download",
+                    data=chat["content"],
+                    file_name=f"response_{idx}.txt",
+                    mime="text/plain",
+                    key=f"download_{idx}",
+                )
+
+    user_question = st.chat_input("Ask a Question from the PDF Files")
+
+    if user_question:
+        st.session_state.chat_history.append({"role": "user", "content": user_question})
+        st.chat_message("user").write(user_question)
+        user_input(user_question)
+        st.experimental_rerun()
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+python-dotenv
+langchain
+langchain_openai
+pypdf
+faiss-cpu
+Flask
+langchain-core
+langchain-community
+streamlit
+chromadb
+PyPDF2
+PyMuPDF
+langchain-openai
+ipykernel