Create app.py

app.py

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+import os
+import fitz  # PyMuPDF for PDF processing
+import faiss
+import numpy as np
+import pickle
+import streamlit as st
+from sentence_transformers import SentenceTransformer
+from groq import Groq
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Initialize Groq Client
+client = Groq(api_key="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")
+
+# Load Sentence Transformer model for embeddings
+embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+# Initialize FAISS index
+INDEX_FILE = "faiss_index.pkl"
+
+def load_faiss_index():
+    if os.path.exists(INDEX_FILE):
+        with open(INDEX_FILE, "rb") as f:
+            return pickle.load(f)
+    return faiss.IndexFlatL2(384)
+
+index = load_faiss_index()
+documents = []
+
+def extract_text_from_pdf(pdf_file):
+    doc = fitz.open(pdf_file)
+    return "\n".join([page.get_text() for page in doc])
+
+def chunk_text(text, chunk_size=500, overlap=100):
+    return [text[i:i + chunk_size] for i in range(0, len(text), chunk_size - overlap)]
+
+def add_to_faiss(text_chunks):
+    global index, documents
+    embeddings = embedding_model.encode(text_chunks)
+    index.add(np.array(embeddings, dtype=np.float32))
+    documents.extend(text_chunks)
+    with open(INDEX_FILE, "wb") as f:
+        pickle.dump(index, f)
+
+def query_faiss(query, top_k=3):
+    query_embedding = embedding_model.encode([query])
+    _, indices = index.search(np.array(query_embedding, dtype=np.float32), top_k)
+    return [documents[i] for i in indices[0] if i < len(documents)]
+
+def query_groq(prompt):
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model="llama-3.3-70b-versatile"
+        )
+        return chat_completion.choices[0].message.content
+    except Exception as e:
+        return f"⚠️ Error: {str(e)}"
+
+# Streamlit UI
+st.set_page_config(page_title="RAG-based PDF Chatbot", page_icon="📄", layout="wide")
+
+st.title("📄 RAG-based PDF Chatbot")
+st.markdown("Talk to your PDFs using AI-powered search!")
+
+with st.sidebar:
+    st.subheader("📤 Upload a PDF")
+    uploaded_file = st.file_uploader("Drag & drop or browse", type="pdf")
+
+if uploaded_file:
+    with st.spinner("Processing your PDF..."):
+        with open("uploaded.pdf", "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        
+        text = extract_text_from_pdf("uploaded.pdf")
+        text_chunks = chunk_text(text)
+        add_to_faiss(text_chunks)
+    
+    st.sidebar.success("✅ PDF uploaded and indexed!")
+    
+    with st.expander("📃 Extracted Text Preview", expanded=False):
+        st.text(text[:1000] + "...")
+    
+    st.markdown("---")
+    st.subheader("🔍 Ask something about the document")
+    query = st.text_input("Type your question below:")
+    
+    if query:
+        retrieved_texts = query_faiss(query)
+        
+        if retrieved_texts:
+            context = "\n".join(retrieved_texts)
+            
+            with st.expander("📖 Retrieved Context", expanded=False):
+                st.text(context[:1000] + "...")
+            
+            response = query_groq(f"Context:\n{context}\n\nUser Query:\n{query}")
+            
+            st.subheader("💬 AI Response")
+            st.markdown(f"**{response}**")
+        else:
+            st.warning("⚠️ No relevant context found in the document!")