Spaces:
Sleeping
Sleeping
File size: 4,221 Bytes
33d6cab 889699f dfd9cf6 606fd71 889699f dfd9cf6 33d6cab 889699f 8e391ed 889699f dfd9cf6 889699f 0ed38c7 dfd9cf6 0ed38c7 dfd9cf6 | 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 | import streamlit as st
from groq import Groq
import os
from PyPDF2 import PdfReader
import requests
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
# ---------------------------
# PAGE CONFIG
# ---------------------------
st.set_page_config(page_title="Krish GPT Multi-Modal RAG", layout="wide")
st.title("🤖 Krish GPT Multi-Modal RAG")
st.caption("PDF + Image OCR + RAG using Groq LLM 🚀")
# ---------------------------
# API KEYS
# ---------------------------
groq_api_key = os.getenv("GROQ_API_KEY")
ocr_api_key = os.getenv("OCR_API_KEY")
if not groq_api_key:
groq_api_key = st.text_input("Enter GROQ API Key", type="password")
if not ocr_api_key:
ocr_api_key = st.text_input("Enter OCR.Space API Key", type="password")
if not groq_api_key or not ocr_api_key:
st.stop()
client = Groq(api_key=groq_api_key)
# ---------------------------
# EMBEDDING MODEL
# ---------------------------
@st.cache_resource
def load_embedder():
return SentenceTransformer("all-MiniLM-L6-v2")
embedder = load_embedder()
# ---------------------------
# OCR Function
# ---------------------------
def ocr_space_image(file, api_key):
url = "https://api.ocr.space/parse/image"
files = {'file': file}
data = {'apikey': api_key, 'language': 'eng'}
r = requests.post(url, files=files, data=data)
try:
result = r.json()
text = result['ParsedResults'][0]['ParsedText']
except:
text = ""
return text
# ---------------------------
# FILE UPLOAD
# ---------------------------
uploaded_file = st.file_uploader(
"Upload PDF or Image", type=["pdf", "png", "jpg", "jpeg"]
)
file_text = ""
if uploaded_file:
if uploaded_file.type == "application/pdf":
reader = PdfReader(uploaded_file)
for page in reader.pages:
t = page.extract_text()
if t:
file_text += t
elif "image" in uploaded_file.type:
file_text = ocr_space_image(uploaded_file, ocr_api_key)
# ---------------------------
# TEXT CHUNKING & FAISS
# ---------------------------
def chunk_text(text, chunk_size=500):
chunks = []
for i in range(0, len(text), chunk_size):
chunks.append(text[i:i+chunk_size])
return chunks
def build_index(chunks):
embeddings = embedder.encode(chunks)
dim = embeddings.shape[1]
index = faiss.IndexFlatL2(dim)
index.add(np.array(embeddings))
return index, embeddings
def search(query, chunks, index):
q_emb = embedder.encode([query])
D, I = index.search(np.array(q_emb), k=min(3, len(chunks)))
results = [chunks[i] for i in I[0]]
return "\n".join(results)
# ---------------------------
# PROCESS FILE
# ---------------------------
if uploaded_file and file_text:
chunks = chunk_text(file_text)
index, embeddings = build_index(chunks)
st.session_state.rag_data = (chunks, index)
# ---------------------------
# CHAT MEMORY
# ---------------------------
if "messages" not in st.session_state:
st.session_state.messages = []
for msg in st.session_state.messages:
with st.chat_message(msg["role"]):
st.markdown(msg["content"])
# ---------------------------
# USER PROMPT
# ---------------------------
prompt = st.chat_input("Ask anything...")
if prompt:
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
context = ""
if "rag_data" in st.session_state:
chunks, index = st.session_state.rag_data
context = search(prompt, chunks, index)
with st.chat_message("assistant"):
try:
response = client.chat.completions.create(
model="llama-3.3-70b-versatile",
messages=[
{"role": "system", "content": f"Context:\n{context}"},
*st.session_state.messages
],
temperature=0.7,
max_tokens=1024
)
reply = response.choices[0].message.content
except Exception as e:
reply = f"❌ Error: {str(e)}"
st.markdown(reply)
st.session_state.messages.append({"role": "assistant", "content": reply}) |