Spaces:
Build error
Build error
File size: 6,698 Bytes
8a82b65 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 |
import os
import requests
import streamlit as st
import pickle
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_groq import ChatGroq
from langchain.document_loaders import PDFPlumberLoader
from langchain_experimental.text_splitter import SemanticChunker
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_chroma import Chroma
from prompts import rag_prompt, relevancy_prompt, relevant_context_picker_prompt, response_synth
# Set API Keys
os.environ["GROQ_API_KEY"] = st.secrets.get("GROQ_API_KEY", "")
# Load LLM models
llm_judge = ChatGroq(model="deepseek-r1-distill-llama-70b")
rag_llm = ChatGroq(model="mixtral-8x7b-32768")
llm_judge.verbose = True
rag_llm.verbose = True
VECTOR_DB_PATH = "/tmp/chroma_db"
CHUNKS_FILE = "/tmp/chunks.pkl"
# Session State Initialization
if "vector_store" not in st.session_state:
st.session_state.vector_store = None
if "documents" not in st.session_state:
st.session_state.documents = None
if "pdf_path" not in st.session_state:
st.session_state.pdf_path = None
if "pdf_loaded" not in st.session_state:
st.session_state.pdf_loaded = False
if "chunked" not in st.session_state:
st.session_state.chunked = False
if "vector_created" not in st.session_state:
st.session_state.vector_created = False
st.title("Blah-2")
# Step 1: Choose PDF Source
pdf_source = st.radio("Upload or provide a link to a PDF:", ["Upload a PDF file", "Enter a PDF URL"], index=0, horizontal=True)
if pdf_source == "Upload a PDF file":
uploaded_file = st.file_uploader("Upload your PDF file", type="pdf")
if uploaded_file:
st.session_state.pdf_path = "temp.pdf"
with open(st.session_state.pdf_path, "wb") as f:
f.write(uploaded_file.getbuffer())
st.session_state.pdf_loaded = False
st.session_state.chunked = False
st.session_state.vector_created = False
elif pdf_source == "Enter a PDF URL":
pdf_url = st.text_input("Enter PDF URL:")
if pdf_url and not st.session_state.pdf_path:
with st.spinner("Downloading PDF..."):
try:
response = requests.get(pdf_url)
if response.status_code == 200:
st.session_state.pdf_path = "temp.pdf"
with open(st.session_state.pdf_path, "wb") as f:
f.write(response.content)
st.session_state.pdf_loaded = False
st.session_state.chunked = False
st.session_state.vector_created = False
st.success("✅ PDF Downloaded Successfully!")
else:
st.error("❌ Failed to download PDF. Check the URL.")
except Exception as e:
st.error(f"❌ Error downloading PDF: {e}")
# Step 2: Load & Process PDF (Only Once)
if st.session_state.pdf_path and not st.session_state.pdf_loaded:
with st.spinner("Loading PDF..."):
try:
loader = PDFPlumberLoader(st.session_state.pdf_path)
docs = loader.load()
st.session_state.documents = docs
st.session_state.pdf_loaded = True
st.success(f"✅ **PDF Loaded!** Total Pages: {len(docs)}")
except Exception as e:
st.error(f"❌ Error processing PDF: {e}")
# Load Cached Chunks if Available
def load_chunks():
if os.path.exists(CHUNKS_FILE):
with open(CHUNKS_FILE, "rb") as f:
return pickle.load(f)
return None
if not st.session_state.chunked: # Ensure chunking only happens once
cached_chunks = load_chunks()
if cached_chunks:
st.session_state.documents = cached_chunks
st.session_state.chunked = True
# Step 3: Chunking (Only Happens Once)
if st.session_state.pdf_loaded and not st.session_state.chunked:
with st.spinner("Chunking the document..."):
try:
model_name = "nomic-ai/modernbert-embed-base"
embedding_model = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={'device': 'cpu'})
text_splitter = SemanticChunker(embedding_model)
if st.session_state.documents:
documents = text_splitter.split_documents(st.session_state.documents)
st.session_state.documents = documents
st.session_state.chunked = True
# Save chunks for persistence
with open(CHUNKS_FILE, "wb") as f:
pickle.dump(documents, f)
st.success(f"✅ **Document Chunked!** Total Chunks: {len(documents)}")
except Exception as e:
st.error(f"❌ Error chunking document: {e}")
# Step 4: Setup Vectorstore
def load_vector_store():
return Chroma(persist_directory=VECTOR_DB_PATH, collection_name="deepseek_collection", embedding_function=HuggingFaceEmbeddings(model_name="nomic-ai/modernbert-embed-base"))
if st.session_state.chunked and not st.session_state.vector_created:
with st.spinner("Creating vector store..."):
try:
if st.session_state.vector_store is None: # Prevent unnecessary reloading
st.session_state.vector_store = load_vector_store()
if len(st.session_state.vector_store.get()["documents"]) == 0: # Prevent duplicate insertions
st.session_state.vector_store.add_documents(st.session_state.documents)
num_documents = len(st.session_state.vector_store.get()["documents"])
st.session_state.vector_created = True
st.success(f"✅ **Vector Store Created!** Total documents stored: {num_documents}")
except Exception as e:
st.error(f"❌ Error creating vector store: {e}")
# Debugging Logs
st.write("📄 **PDF Loaded:**", st.session_state.pdf_loaded)
st.write("🔹 **Chunked:**", st.session_state.chunked)
st.write("📂 **Vector Store Created:**", st.session_state.vector_created)
# ----------------- Query Input -----------------
query = st.text_input("🔍 Ask a question about the document:")
if query:
with st.spinner("🔄 Retrieving relevant context..."):
retriever = st.session_state.vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
contexts = retriever.invoke(query)
# Debugging: Check what was retrieved
st.write("Retrieved Contexts:", contexts)
st.write("Number of Contexts:", len(contexts))
context = [d.page_content for d in contexts]
# Debugging: Check extracted context
st.write("Extracted Context (page_content):", context)
st.write("Number of Extracted Contexts:", len(context))
------ |