Update app.py

app.py

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+import os
+import streamlit as st
+import PyPDF2
+from langchain_community.embeddings import HuggingFaceEmbeddings
+import faiss
+from groq import Groq
+import numpy as np
+
+# Load your environment variables
+API = os.environ['GROQ_API_KEY'] = "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"
+
+# Initialize Groq client
+client = Groq(api_key=API)
+
+# Initialize HuggingFace embedding model from langchain_community
+embedding_model_name = "sentence-transformers/all-MiniLM-L6-v2"
+embedding_model = HuggingFaceEmbeddings(model_name=embedding_model_name)
+
+# Determine the vector size dynamically by generating a sample embedding
+sample_embedding = embedding_model.embed_query("test")
+dimension = len(sample_embedding)
+
+# Initialize FAISS
+index = faiss.IndexFlatL2(dimension)
+
+# Streamlit front-end
+st.title("RAG-based PDF Query Application")
+
+# File uploader
+uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
+if uploaded_file is not None:
+    # Extract text from PDF
+    pdf_reader = PyPDF2.PdfReader(uploaded_file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+
+    st.write("PDF Uploaded Successfully!")
+    
+    # Create chunks
+    def create_chunks(text, chunk_size=500):
+        words = text.split()
+        chunks = [" ".join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]
+        return chunks
+    
+    chunks = create_chunks(text)
+    st.write(f"Created {len(chunks)} chunks.")
+
+    # Generate embeddings
+    embeddings = [embedding_model.embed_query(chunk) for chunk in chunks]
+    embeddings = np.array(embeddings, dtype=np.float32)  # Convert to float32
+    faiss.normalize_L2(embeddings)
+    index.add(embeddings)
+    st.write("Embeddings generated and stored in FAISS.")
+
+    # Query input
+    user_query = st.text_input("Enter your query:")
+    if user_query:
+        # Query embedding
+        query_embedding = embedding_model.embed_query(user_query)
+        query_embedding = np.array([query_embedding], dtype=np.float32)  # Convert to float32
+        faiss.normalize_L2(query_embedding)
+        
+        # Search for similar chunks
+        k = 3  # Number of nearest neighbors
+        distances, indices = index.search(query_embedding, k)
+        relevant_chunks = [chunks[i] for i in indices[0]]
+
+        # Pass to Groq API
+        prompt = "\n\n".join(relevant_chunks) + f"\n\nUser Query: {user_query}"
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {"role": "user", "content": prompt}
+            ],
+            model="llama3-8b-8192"
+        )
+        response = chat_completion.choices[0].message.content
+        
+        # Display response
+        st.write("### Response")
+        st.write(response)