Create app.py

app.py

@@ -0,0 +1,97 @@
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_community.embeddings.spacy_embeddings import SpacyEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.tools.retriever import create_retriever_tool
+from dotenv import load_dotenv
+from langchain_anthropic import ChatAnthropic
+from langchain.agents import AgentExecutor, create_tool_calling_agent
+
+load_dotenv()
+embeddings = SpacyEmbeddings(model_name="en_core_web_sm")
+def pdf_read(pdf_doc):
+    text = ""
+    for pdf in pdf_doc:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+
+
+def get_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+def vector_store(text_chunks):
+    
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    vector_store.save_local("faiss_db")
+
+
+def get_conversational_chain(tools,ques):
+    #os.environ["ANTHROPIC_API_KEY"]=os.getenv["ANTHROPIC_API_KEY"]
+    llm = ChatAnthropic(model="claude-3-sonnet-20240229", temperature=0, api_key=os.getenv("ANTHROPIC_API_KEY"),verbose=True)
+
+    prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            """You are a helpful assistant. Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
+    provided context just say, "answer is not available in the context", don't provide the wrong answer""",
+        ),
+        ("placeholder", "{chat_history}"),
+        ("human", "{input}"),
+        ("placeholder", "{agent_scratchpad}"),
+    ]
+)
+    tool=[tools]
+    agent = create_tool_calling_agent(llm, tool, prompt)
+
+    agent_executor = AgentExecutor(agent=agent, tools=tool, verbose=True)
+    response=agent_executor.invoke({"input": ques})
+    print(response)
+    st.write("Reply: ", response['output'])
+
+
+
+def user_input(user_question):
+    
+    
+    
+    new_db = FAISS.load_local("faiss_db", embeddings,allow_dangerous_deserialization=True)
+    
+    retriever=new_db.as_retriever()
+    retrieval_chain= create_retriever_tool(retriever,"pdf_extractor","This tool is to give answer to queries from the pdf")
+    get_conversational_chain(retrieval_chain,user_question)
+
+
+
+
+
+def main():
+    st.set_page_config("Chat PDF")
+    st.header("RAG based Chat with PDF")
+
+    user_question = st.text_input("Ask a Question from the PDF Files")
+
+    if user_question:
+        user_input(user_question)
+
+    with st.sidebar:
+        st.title("Menu:")
+        pdf_doc = 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 = pdf_read(pdf_doc)
+                text_chunks = get_chunks(raw_text)
+                vector_store(text_chunks)
+                st.success("Done")
+
+if __name__ == "__main__":
+    main()