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import streamlit as st
from groq import Groq
from PIL import Image
import os
from dotenv import load_dotenv
import base64
import io
# Load environment variables
load_dotenv()
api_key = os.getenv("GROQ_API_KEY")
client = Groq(api_key=api_key)
# Streamlit page configuration
st.set_page_config(
page_title="Llama OCR",
page_icon="🦙",
layout="wide",
initial_sidebar_state="expanded"
)
def main_content():
st.title("🦙 Llama OCR")
st.markdown('<p style="margin-top: -20px;">Extract structured text from images using Llama 3.2 Vision!</p>', unsafe_allow_html=True)
st.markdown("---")
col1, col2 = st.columns([6, 1])
with col2:
if st.button("Clear 🗑️"):
if 'ocr_result' in st.session_state:
del st.session_state['ocr_result']
st.rerun()
if 'ocr_result' in st.session_state:
st.markdown("### 🎯 **Extracted Text**")
st.markdown(st.session_state['ocr_result'], unsafe_allow_html=True)
def sidebar_content():
with st.sidebar:
st.header("📥 Upload Image")
if 'ocr_result' not in st.session_state:
st.write("### Please upload an image to extract text.")
uploaded_file = st.file_uploader("Choose an image...", type=['png', 'jpg', 'jpeg'])
if uploaded_file:
display_uploaded_image(uploaded_file)
if uploaded_file and st.button("Extract Text 🔍") and 'ocr_result' not in st.session_state:
with st.spinner("Processing image... Please wait."):
process_image(uploaded_file)
if not uploaded_file and 'ocr_result' not in st.session_state:
st.sidebar.empty()
def display_uploaded_image(uploaded_file):
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_container_width=True)
def encode_image(uploaded_file):
image = Image.open(uploaded_file)
buffered = io.BytesIO()
image.save(buffered, format=image.format)
img_byte_array = buffered.getvalue()
return base64.b64encode(img_byte_array).decode('utf-8'), image.format
def process_image(uploaded_file):
if uploaded_file:
base64_image, image_format = encode_image(uploaded_file)
mime_type = f"image/{image_format.lower()}"
base64_url = f"data:{mime_type};base64,{base64_image}"
with st.spinner("Generating response... This may take a moment."):
try:
response = client.chat.completions.create(
model="llama-3.2-11b-vision-preview",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Analyze the text in the provided image. Extract all readable content and present it in a structured Markdown format. Use headings, lists, or code blocks as appropriate for clarity and organization."},
{"type": "image_url", "image_url": {"url": base64_url}},
]
}
],
temperature=0.2,
max_tokens=200,
top_p=0.5,
stream=False
)
message_content = response.choices[0].message.content
st.session_state['ocr_result'] = message_content
except Exception as e:
st.error(f"Error during text extraction: {e}")
# Corrected execution order: process sidebar first, then main content
if __name__ == "__main__":
sidebar_content()
main_content() |