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import streamlit as st |
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from PIL import Image |
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from PyPDF2 import PdfReader |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI |
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from langchain.vectorstores import FAISS |
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import os |
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import google.generativeai as genai |
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from dotenv import load_dotenv |
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load_dotenv() |
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genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) |
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def get_pdf_text(pdf_docs): |
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text="" |
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for pdf in pdf_docs: |
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pdf_reader= PdfReader(pdf) |
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for page in pdf_reader.pages: |
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text+= page.extract_text() |
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return text |
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def get_text_chunks(text): |
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000) |
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chunks = text_splitter.split_text(text) |
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return chunks |
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def get_vector_store(text_chunks): |
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embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") |
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vector_store = FAISS.from_texts(text_chunks, embedding=embeddings) |
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vector_store.save_local("faiss_index") |
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model = genai.GenerativeModel("gemini-pro-vision") |
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def get_gemini_response(input, image=None): |
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if image is not None and model.is_image_model: |
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response = model.generate_content([input, image]) |
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else: |
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response = model.generate_content(input) |
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return response.text |
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st.set_page_config(page_title='Combined Streamlit Application') |
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st.header("Streamlit Application") |
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applications = { |
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"PDF Chat": "pdf_chat", |
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"Image Chat": "image_chat", |
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} |
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selected_app = st.sidebar.selectbox("Select Application", list(applications.keys())) |
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def pdf_chat(): |
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st.header("PDF Chat Application") |
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user_question = st.text_input("Ask a Question from the PDF Files") |
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if user_question: |
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user_input(user_question) |
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pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True) |
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if st.button("Submit & Process"): |
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with st.spinner("Processing..."): |
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raw_text = get_pdf_text(pdf_docs) |
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text_chunks = get_text_chunks(raw_text) |
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get_vector_store(text_chunks) |
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st.success("Done") |
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def image_chat(): |
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st.header("Image Chat Application") |
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input_text = st.text_input("Input for Gemini Pro:", key="input_gemini") |
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uploaded_file = st.file_uploader("Choose an image...", type="jpg") |
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if uploaded_file is not None: |
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image = Image.open(uploaded_file) |
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st.image(image, caption="Uploaded Image", use_column_width=True) |
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submit_gemini = st.button("Ask Gemini Pro") |
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if submit_gemini: |
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response_gemini = get_gemini_response(input_text, image) |
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st.subheader("Gemini Pro Response:") |
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st.write(response_gemini) |
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selected_app_func = { |
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"PDF Chat": pdf_chat, |
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"Image Chat": image_chat, |
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} |
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selected_app_func[selected_app]() |
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