Model-Files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Deepseek.pdf filter=lfs diff=lfs merge=lfs -text

Datapreprocessing.py

@@ -0,0 +1,10 @@
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+def PreprocessingData(documents, chunk_size=1500, chunk_overlap=40):
+    """Chunk documents into smaller parts for embedding."""
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap
+    )
+    chunked_docs = text_splitter.split_documents(documents)
+    return chunked_docs

Deepseek.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52d8ca3ac93e88cef9944e1fd03b0e04aec5954495a8250fb2fadf8fa20a4dad
+size 1312189

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,14 +1,115 @@
+## 📄 Multi-Modal RAG PDF Chatbot
+
+A Streamlit application that allows you to **upload a PDF**, ask questions about its content, and get accurate responses using a **Multi-Modal Retrieval-Augmented Generation (RAG)** pipeline powered by **Groq's Gemma-2 9B model**.
+
+---
+
+### 🚀 Features
+
+- 📁 Upload any PDF
+- 🔍 Intelligent chunking and embedding
+- 🧠 Ask natural language questions about your PDF
+- ⚡ Powered by FAISS + HuggingFace + Groq LLM
+- 🧠 Caches session so PDF isn't reprocessed on every query
+
+---
+
+### 🛠️ Installation (with `venv`)
+
+1. **Clone the repo:**
+
+```bash
+git clone https://github.com/Warishayat/Multimodel-Rag-Application01.git
+cd Multimodal-Rag-Application01
+```
+
+2. **Create and activate a virtual environment:**
+
+```bash
+python -m venv venv
+# Activate:
+# On Windows
+venv\Scripts\activate
+# On macOS/Linux
+source venv/bin/activate
+```
+
+3. **Install dependencies:**
+
+```bash
+pip install -r requirements.txt
+```
+
+4. **Set up your `.env` file:**
+
+Create a `.env` file in the root directory:
+
+```
+GROQ_API_KEY=your_groq_api_key_here
+```
+
+---
+
+### 📦 Project Structure
+
+```
+📁 Multimodal-Rag-Application01
+├── main.py                 # Streamlit frontend
+├── pdfparsing.py          # PDF parser using pymupdf4llm
+├── Datapreprocessing.py   # Chunking & text cleaning
+├── vectorstore.py         # Embedding & FAISS logic
+├── .env                   # API keys
+├── requirements.txt       # Python dependencies
+└── README.md              # You're here!
+```
+
 ---
-title: Multi Model Pdf Chat
-emoji: 😻
-colorFrom: gray
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.44.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: An end-to-end Multimodal RAG application combining text, ima
+
+### ▶️ Run the App
+
+```bash
+streamlit run main.py
+```
+
+Then open `http://localhost:8501` in your browser.
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+### 🧪 Example Queries
+
+After uploading a PDF, try asking:
+- "What is the summary of section 3?"
+- "List all benchmarks mentioned."
+- "How is this model different from others?"
+
+---
+
+### 💡 Tips
+
+- PDF is processed only once per session using `st.session_state`.
+- Uses `RecursiveCharacterTextSplitter` for effective chunking.
+- Embedding with `HuggingFaceEmbeddings`.
+
+---
+
+### 📋 Requirements
+
+Make sure your `requirements.txt` includes at least:
+
+```txt
+streamlit
+python-dotenv
+langchain
+langchain-community
+langchain-groq
+faiss-cpu
+pymupdf4llm
+```
+
+---
+
+### 📬 Credits
+
+Built with ❤️ by Waris Hayat Abbasi.
+
+---

RetrivalChain.py

@@ -0,0 +1,47 @@
+from langchain_groq import ChatGroq
+from dotenv import load_dotenv
+import os
+from pdfparsing import ExtractDatafrompdf
+from Datapreprocessing import PreprocessingData
+from vectorstore import embeddings, vectorstore
+from langchain.chains import RetrievalQA
+
+# Load environment
+load_dotenv()
+Groq_api_key = os.environ.get("GROQ_API_KEY")
+
+# LLM setup
+Model = ChatGroq(
+    api_key=Groq_api_key,
+    model="qwen-qwq-32b",  
+    temperature=0.2,
+)
+
+def GenrateResponse(query, retrive):
+    chain = RetrievalQA.from_chain_type(
+        llm=Model,
+        chain_type="stuff",
+        retriever=retrive,
+    )
+    return chain.invoke({"query": query})
+
+if __name__ == "__main__":
+    pdf_path = r"C:\Users\HP\Desktop\MultiModel-Rag\Multimodel-Rag-Application01\Deepseek.pdf"
+
+    print("Extracting PDF...")
+    documents = ExtractDatafrompdf(pdf_path)
+
+    print("Chunking Data...")
+    chunked_data = PreprocessingData(documents)
+    print(f"Total Chunks: {len(chunked_data)}")
+
+    print("Vectorizing...")
+    retriever = vectorstore(data=chunked_data, embeddings=embeddings())
+
+    # Example query
+    query = "what are the benchamrk of deepseek r1?"
+    print("Answering Query...")
+    result = GenrateResponse(query=query, retrive=retriever)
+
+    print("Response:")
+    print(result["result"])

main.py

@@ -0,0 +1,66 @@
+import streamlit as st
+import os
+import tempfile
+from dotenv import load_dotenv
+from langchain_groq import ChatGroq
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from Datapreprocessing import PreprocessingData
+from pdfparsing import ExtractDatafrompdf
+
+load_dotenv()
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+
+st.set_page_config(page_title="📄 Chat with PDF", layout="wide")
+
+# Sidebar for PDF Upload
+st.sidebar.title("📂 Upload your PDF")
+uploaded_file = st.sidebar.file_uploader("Choose a PDF", type="pdf")
+
+# LLM and Embeddings - cached
+@st.cache_resource
+def get_embeddings():
+    return HuggingFaceEmbeddings()
+
+@st.cache_resource
+def get_llm():
+    return ChatGroq(api_key=GROQ_API_KEY, model="gemma2-9b-it", temperature=0.2)
+
+# Build Retrieval Chain
+def get_chain(retriever):
+    llm = get_llm()
+    return RetrievalQA.from_chain_type(llm=llm, retriever=retriever, chain_type="stuff")
+
+# PDF processing pipeline
+def process_pdf_and_create_chain(uploaded_file):
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+        tmp.write(uploaded_file.read())
+        tmp_path = tmp.name
+
+    documents = ExtractDatafrompdf(tmp_path)
+    chunks = PreprocessingData(documents)
+    embedder = get_embeddings()
+    retriever = FAISS.from_documents(chunks, embedder).as_retriever(search_type="similarity", search_kwargs={"k": 1})
+    return get_chain(retriever)
+
+# Main UI
+st.title("📄 Ask Questions About Your PDF")
+
+if uploaded_file:
+    if "chain" not in st.session_state:
+        st.success("PDF uploaded successfully! Processing...")
+        with st.spinner("Extracting and chunking PDF..."):
+            st.session_state.chain = process_pdf_and_create_chain(uploaded_file)
+        st.success("Ready to chat with your PDF!")
+    else:
+        st.sidebar.info("Using cached PDF session.")
+
+    user_query = st.text_input("Ask a question about your PDF:")
+    if user_query:
+        with st.spinner("Generating answer..."):
+            result = st.session_state.chain.invoke({"query": user_query})
+            st.markdown("### 📌 Answer:")
+            st.write(result["result"])
+else:
+    st.info("📤 Upload a PDF from the sidebar to begin.")

pdfparsing.py

@@ -0,0 +1,23 @@
+import pymupdf4llm
+from langchain.docstore.document import Document
+
+def ExtractDatafrompdf(pdf_path):
+    """Extract PDF data using pymupdf4llm and return LangChain Documents."""
+    md_pages = pymupdf4llm.to_markdown(
+        pdf_path,
+        write_images=True,
+        image_path="images",
+        image_format="png",
+        page_chunks=True
+    )
+
+    
+    print("First page structure:", md_pages[0])
+
+    documents = []
+    for page in md_pages:
+        text = page["text"]  
+        page_num = md_pages.index(page) + 1  
+        documents.append(Document(page_content=text, metadata={"page_number": page_num}))
+    
+    return documents

vectorstore.py

@@ -0,0 +1,18 @@
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+
+def embeddings():
+    print("Generating Embeddings...")
+    return HuggingFaceEmbeddings()
+
+def vectorstore(data, embeddings):
+    print("Creating VectorStore...")
+    vectorstore = FAISS.from_documents(
+        documents=data,
+        embedding=embeddings
+    )
+    retriever = vectorstore.as_retriever(
+        search_type="similarity",
+        search_kwargs={"k": 2}  # You can tune this
+    )
+    return retriever