Added PDF Wizard

Generative-AI/PDF Wizard/.env

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+GOOGLE_API_KEY = "Your_GEMIN_API_KEY";

Generative-AI/PDF Wizard/app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import google.generativeai as genai
+from langchain.vectorstores import FAISS
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+
+
+load_dotenv()
+api_key = os.getenv("GOOGLE_API_KEY")
+if not api_key:
+    raise ValueError("GOOGLE_API_KEY environment variable is missing. Please add it to your .env file.")
+genai.configure(api_key=api_key)
+
+
+##Function that reads the pdd goes through each and every page
+def get_pdf_text(pdf_docs):
+    if not pdf_docs:
+        return ""
+    text=""
+    for pdf in pdf_docs:
+        pdf_reader= PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text+= page.extract_text()
+        # Optionally wrap the above block in try-except if needed:
+        # try:
+        #     pdf_reader = PdfReader(pdf)
+        #     for page in pdf_reader.pages:
+        #         text += page.extract_text()
+        # except Exception as e:
+        #     st.error(f"Error reading PDF '{pdf.name}': {str(e)}")
+    return  text
+
+
+##Function that breaks text into chunks
+def get_text_chunks(text):
+    # Adjust chunk size and overlap as needed
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+##Function that saves the data we got from conversation to local(here), generally stored in data base
+#converting into chunks -> storing data in faiss vector
+def get_vector_store(text_chunks, embedding_model="models/embedding-001", store_dir="faiss_index"):
+    """Create and save vector store from text chunks.
+
+    Args:
+        text_chunks: List of text chunks to embed
+        embedding_model: Name of the embedding model to use
+        store_dir: Directory to save the vector store
+
+    Returns:
+        None
+    """
+    if not text_chunks:
+        st.warning("No text to process. Please check the PDF content.")
+        return
+
+    try:
+        embeddings = GoogleGenerativeAIEmbeddings(model=embedding_model)
+        vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+
+        # Ensure the directory exists
+        if not os.path.exists(store_dir):
+            os.makedirs(store_dir)
+
+        # Save the vector store index in the directory
+        vector_store.save_local(store_dir)
+    except Exception as e:
+        st.error(f"Error creating vector store: {str(e)}")
+
+
+#Function to give the prompt and ask the bot to act accordingly, giving the gemini model
+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
+    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
+    Context:\n {context}?\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+    model = ChatGoogleGenerativeAI(model="gemini-1.5-flash",temperature=0.3)
+
+    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
+    return chain
+
+
+##Function for user input, question
+#give the question-> do a similarity search on all the faiss vectors-> go with converstional chain-> response from chain
+def user_input(user_question):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+
+    # Check if the faiss_index file exists before loading
+    if not os.path.exists("faiss_index/index.faiss"):
+        st.error("FAISS index file not found. Please process the PDF files first.")
+        return
+
+    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"])
+
+
+##Main Function for streamlit app
+def main():
+    st.set_page_config("PDF Wizard")
+    st.header("Chat with multiple PDFs📄")
+
+    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_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")
+
+
+
+if __name__ == "__main__":
+    main()

Generative-AI/PDF Wizard/faiss_index/app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import google.generativeai as genai
+from langchain.vectorstores import FAISS
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from dotenv import load_dotenv
+
+
+load_dotenv()
+os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+
+
+
+##Function that reads the pdd goes through each and every page
+def get_pdf_text(pdf_docs):
+    text=""
+    for pdf in pdf_docs:
+        pdf_reader= PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text+= page.extract_text()
+    return  text
+
+
+
+##Function that breaks text into chunks
+def get_text_chunks(text):
+    # Adjust chunk size and overlap as needed
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+
+
+##Function that saves the data we got from conversation to local(here), generally stored in data base
+#converting into chunks -> storing data in faiss vector
+def get_vector_store(text_chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+
+    # Ensure the directory exists
+    if not os.path.exists("faiss_index"):
+        os.makedirs("faiss_index")
+
+    # Save the vector store index in the directory
+    vector_store.save_local("faiss_index")
+
+
+
+
+#Function to give the prompt and ask the bot to act accordingly, giving the gemini model
+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
+    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
+    Context:\n {context}?\n
+    Question: \n{question}\n
+
+    Answer:
+    """
+    model = ChatGoogleGenerativeAI(model="gemini-1.5-flash",temperature=0.3)
+
+    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
+    return chain
+
+
+
+
+
+##Function for user input, question
+#give the question-> do a similarity search on all the faiss vectors-> go with converstional chain-> response from chain
+def user_input(user_question):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+
+    # Check if the faiss_index file exists before loading
+    if not os.path.exists("faiss_index/index.faiss"):
+        st.error("FAISS index file not found. Please process the PDF files first.")
+        return
+
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    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"])
+
+
+
+
+##Main Function for streamlit app
+def main():
+    st.set_page_config("PDF Wizard")
+    st.header("Chat with multiple PDFs📄")
+
+    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_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")
+
+
+
+if __name__ == "__main__":
+    main()

Generative-AI/PDF Wizard/readme_pdf_wiz.md

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+# PDF Wizard
+
+
+📌 Overview
+-----------
+
+PDF Wizard is an AI-powered Streamlit web application that allows users to chat with multiple PDFs. It processes uploaded PDF files, extracts their text, converts them into embeddings, stores them in a FAISS vector database, and answers user queries based on the stored content using Google's Gemini AI.
+
+🚀 Features
+-----------
+
+*   📂 Upload multiple PDF files
+*   🧠 AI-powered chatbot using Google Gemini API
+*   🔍 Search and retrieve context-based answers
+*   📚 FAISS vector database for efficient document searching
+*   ⚡ Streamlit-based interactive UI
+
+
+🛠️ Tech Stack
+--------------
+
+*   Python
+*   Streamlit
+*   FAISS
+*   Google Generative AI (Gemini API)
+*   PyPDF2
+*   LangChain
+*   dotenv
+
+
+📦 Installation
+---------------
+1. Clone the Repo
+```
+git clone https://github.com/UTSAVS26/PyVerse.git
+cd Generative-AI 
+cd PDF-Wizard
+ ```   
+2. Activate the environment  
+```
+python -m venv venv
+```
+```
+source venv/bin/activate # For macOS/Linux
+venv\Scripts\activate # For Windows
+```
+3. Install dependencies
+`pip install -r requirements.txt`
+
+4.  **Set Up Environment Variables**
+
+    *   Create a .env file in the project root.
+    *   GOOGLE\_API\_KEY= **your\_google\_api\_key**
+
+
+🎯 Usage
+--------
+
+1.  Run the application
+`streamlit run app.py`
+
+2.  **Upload PDF files** via the sidebar.
+
+3.  **Ask questions** based on the uploaded PDFs.
+
+📂 ScreenShots
+---------------------
+![alt text](image.png)
+
+📂 Project Structure
+--------------------
+```
+PDF-Wizard
+│-- faiss_index/         # Directory to store FAISS vectors
+│-- app.py               # Main Streamlit app
+│-- requirements.txt     # Required dependencies
+│-- .env                 # API keys and environment variables
+│-- README.md            # Project documentation
+```
+
+🌟 Acknowledgments
+------------------
+
+*   [Streamlit](https://streamlit.io/)
+*   [FAISS](https://faiss.ai/)
+*   [Google Generative AI](https://ai.google.dev/)
+
+👤 Contributor 
+---------------- 
+- **Name:** Arnab Ghosh
+- **GitHub:** [tulug-559](https://github.com/tulu-g559)  
+- **Contact:** [email]([email protected])  

Generative-AI/PDF Wizard/requirements.txt

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+streamlit
+google-generativeai
+python-dotenv
+langchain
+PyPDF2
+faiss-cpu
+langchain_google_genai