Upload app.py

app.py

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+import os
+import streamlit as st
+from PyPDF2 import PdfReader
+import google.generativeai as genai
+from langchain.vectorstores import FAISS
+from langchain.prompts import PromptTemplate
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+
+def process_pdf_files(pdf_files, embedding_model_name):
+    text = ""
+    for pdf in pdf_files:
+        reader = PdfReader(pdf)
+        for page in reader.pages:
+            text += page.extract_text()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=500)
+    text_chunks = text_splitter.split_text(text)
+    embeddings = GoogleGenerativeAIEmbeddings(model=embedding_model_name)
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    vector_store.save_local("pdf_database")
+    return vector_store
+
+def setup_qa_chain(chat_model_name):
+    prompt_template = """
+    Give answer to the asked question using the provided custom knowledge or given context only and if there is no related content then simply say "Your document dont contain related context to answer". Make sure to not answer incorrect.\n\n
+    Context:\n{context}\n
+    Question:\n{question}\n
+    Answer:
+    """
+    model = ChatGoogleGenerativeAI(model=chat_model_name, temperature=0.3)
+    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    return load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
+def get_response(user_question, chat_model_name, embedding_model_name):
+    embeddings = GoogleGenerativeAIEmbeddings(model=embedding_model_name)
+    vector_store = FAISS.load_local("pdf_database", embeddings, allow_dangerous_deserialization=True)
+    docs = vector_store.similarity_search(user_question)
+    chain = setup_qa_chain(chat_model_name)
+    response = chain(
+        {"input_documents": docs, "question": user_question},
+        return_only_outputs=True
+    )
+    return response["output_text"]
+
+def main():
+    st.set_page_config(page_title="Talk to PDF", layout="wide")
+
+    st.markdown(
+        f"""
+        <style>
+        .stApp {{
+            background: url(data:image/png;base64,{get_base64_of_image('image.png')});
+            background-size: cover
+        }}
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+    st.title("Chat using Google Gemini Models")
+
+    st.subheader("Upload your PDF Files")
+    pdf_files = st.file_uploader("Upload your files", accept_multiple_files=True)
+    if st.button("Submit data") and pdf_files:
+        with st.spinner("Processing the data . . ."):
+            process_pdf_files(pdf_files, embedding_model_name)
+            st.success("Files submitted successfully")
+
+    st.sidebar.header("Configuration")
+    api_key = st.sidebar.text_input("Google API Key:", type="password")
+
+    default_chat_models = ["gemini-pro", "chat-model-2", "chat-model-3"]
+    selected_chat_model = st.sidebar.selectbox("Select a chat model", default_chat_models, index=0)
+    custom_chat_model = st.sidebar.text_input("Or enter a custom chat model name")
+
+    if custom_chat_model:
+        chat_model_name = custom_chat_model
+    else:
+        chat_model_name = selected_chat_model
+
+    default_embedding_models = ["models/embedding-001", "embedding-model-2", "embedding-model-3"]
+    selected_embedding_model = st.sidebar.selectbox("Select an embedding model", default_embedding_models, index=0)
+    custom_embedding_model = st.sidebar.text_input("Or enter a custom embedding model name")
+
+    if custom_embedding_model:
+        embedding_model_name = custom_embedding_model
+    else:
+        embedding_model_name = selected_embedding_model
+
+    if api_key:
+        genai.configure(api_key=api_key)
+        
+        user_question = st.text_input("Ask questions from your custom knowledge base!")
+
+        if user_question:
+            with st.spinner("Generating response..."):
+                response = get_response(user_question, chat_model_name, embedding_model_name)
+                st.write("**Reply:** ", response)
+
+    else:
+        st.sidebar.warning("Please enter your Google API key!")
+        
+    st.markdown("---")
+    st.write("Happy to Connect:")
+    kaggle, linkedin, google_scholar, youtube, github = st.columns(5)
+
+    image_urls = {
+            "kaggle": "https://www.kaggle.com/static/images/site-logo.svg",
+            "linkedin": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/ca/LinkedIn_logo_initials.png/600px-LinkedIn_logo_initials.png",
+            "google_scholar": "https://upload.wikimedia.org/wikipedia/commons/thumb/c/c7/Google_Scholar_logo.svg/768px-Google_Scholar_logo.svg.png",
+            "youtube": "https://upload.wikimedia.org/wikipedia/commons/thumb/7/72/YouTube_social_white_square_%282017%29.svg/640px-YouTube_social_white_square_%282017%29.svg.png",
+            "github": "https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png"
+        }
+
+    social_links = {
+            "kaggle": "https://www.kaggle.com/muhammadimran112233",
+            "linkedin": "https://www.linkedin.com/in/muhammad-imran-zaman",
+            "google_scholar": "https://scholar.google.com/citations?user=ulVFpy8AAAAJ&hl=en",
+            "youtube": "https://www.youtube.com/@consolioo",
+            "github": "https://github.com/Imran-ml"
+        }
+
+    kaggle.markdown(f'<a href="{social_links["kaggle"]}"><img src="{image_urls["kaggle"]}" width="50" height="50"></a>', unsafe_allow_html=True)
+    linkedin.markdown(f'<a href="{social_links["linkedin"]}"><img src="{image_urls["linkedin"]}" width="50" height="50"></a>', unsafe_allow_html=True)
+    google_scholar.markdown(f'<a href="{social_links["google_scholar"]}"><img src="{image_urls["google_scholar"]}" width="50" height="50"></a>', unsafe_allow_html=True)
+    youtube.markdown(f'<a href="{social_links["youtube"]}"><img src="{image_urls["youtube"]}" width="50" height="50"></a>', unsafe_allow_html=True)
+    github.markdown(f'<a href="{social_links["github"]}"><img src="{image_urls["github"]}" width="50" height="50"></a>', unsafe_allow_html=True)
+    st.markdown("---")
+
+def get_base64_of_image(image_path):
+    import base64
+    with open(image_path, "rb") as image_file:
+        base64_str = base64.b64encode(image_file.read()).decode()
+    return base64_str
+
+if __name__ == "__main__":
+    main()