Upload 3 files

.env

@@ -0,0 +1 @@
+GOOGLE_API_KEY="AIzaSyDXQW8-SKLROfSGvJcQOXOl-iE9rvKV2Zk"

app.py

@@ -0,0 +1,98 @@
+import streamlit as st
+from pypdf import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_google_genai import ChatGoogleGenerativeAI
+import google.generativeai as genai
+
+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
+
+load_dotenv()
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+print('Here am I ...1')
+def get_pdf_text(pdf_docs):
+    text =""
+    print('Here am I ...1.1')
+    for pdf in pdf_docs:
+        print('Here am I ...1.2')
+        pdf_reader=PdfReader(pdf)
+        print('Here am I ...1.3')
+        for page in pdf_reader.pages:
+            print('for pages in...text+=')
+            text+=page.extract_text()
+            print('Here am I ...2')
+    return text
+
+def get_text_chunks(text):
+    print('Here am I ...3')
+    text_splitter=RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks=text_splitter.split_text(text)
+    return chunks
+
+def get_vector_store(text_chunks):
+    embeddings=GoogleGenerativeAIEmbeddings(model="models/embeddings-001")
+    vector_stores=FAISS.from_texts(text_chunks,embedding=embeddings)
+    vector_stores.save_local("faiss_index")
+
+def get_conversational_chain():
+    Prompt_template="""
+    Answer the question as detailed as possible from the provided context, make sure to provide all the details.
+    if the answer is not in the provided context just say "answer is not available in the context",dont provide the wrong answer.\n\n_
+    Context:\n{context}?\n
+    Question:\n{question}\n
+
+    Answer:
+    """
+ #   print('Here am I ...4')
+    model=ChatGoogleGenerativeAI(model="gemini-pro",temperature=0.3)
+    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 = GoogleGenerativeAIEmbeddings(model="models/embeddings-001")
+
+    new_db = FAISS.load_local("faiss_index",embeddings)
+    docs = new_db.similarity_search(user_question)
+
+    chain = get_conversational_chain()
+
+    response = chain(
+        {"input_documents":docs,"question":user_question},
+        return_only_outputs=True)
+    
+    print(response)
+    st.write("Reply:", response["Output_text"])
+# print('Here am I ...just bef main')
+def main():
+#   print('Here am I ...inside main')
+    st.set_page_config("Chat with multiple PDF")
+    st.header("Chat with multiple PDF using Gemini AI")
+    print('Here am I ...5')
+    user_question = st.text_input("Ask a question from a PDF files")
+
+    if user_question:
+        user_input(user_question)
+    print('Here am I ...6')
+    with st.sidebar:
+        st.title("Menu:")
+        pdf_docs = st.file_uploader("Upload your PDF files and click on the submit button")
+        if st.button("Submit and Process"):
+            with st.spinner("Processing..."):
+                print('Just before get pdf text call...')
+                raw_text = get_pdf_text(pdf_docs)
+                print('Just bef get text chunks cal...')
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks)
+                st.success("Done")
+                st.print("In main st.print")
+#print('Here am I ...the end')
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,12 @@
+streamlit
+google-generativeai
+python-dotenv
+langchain
+PyPDF
+chromadb
+faiss-cpu
+langchain_google_genai
+langchain_community
+
+
+