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import streamlit as st
import os
import glob
import base64
import json
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from langchain_openai import ChatOpenAI
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.runnables import chain
from PIL import Image as PILImage
from io import BytesIO
# Streamlit title
st.title("🚗 Vehicle Information Extraction from Images 🚙")
# Custom CSS Styling for the app
st.markdown("""
<style>
body {
background-color: #f0f8ff;
font-family: 'Arial', sans-serif;
color: #333;
}
.stButton button {
background-color: #1E90FF;
color: white;
border-radius: 5px;
font-size: 16px;
padding: 10px 20px;
transition: transform 0.2s ease-in-out;
}
.stButton button:hover {
background-color: #4682b4;
transform: scale(1.05);
}
.stTitle {
font-size: 36px;
font-weight: bold;
color: #1E90FF;
}
.stSubheader {
color: #4682b4;
font-size: 24px;
font-weight: 600;
text-align: center;
}
.stImage {
border-radius: 10px;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
}
.stJson {
background-color: #f8f9fa;
padding: 15px;
border-radius: 5px;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
font-size: 16px;
}
.grid-container {
display: grid;
grid-template-columns: repeat(3, 1fr);
gap: 10px;
padding: 10px;
}
.grid-container img {
border-radius: 10px;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
transition: transform 0.2s ease-in-out;
}
.grid-container img:hover {
transform: scale(1.05);
}
@keyframes fadeIn {
from {
opacity: 0;
}
to {
opacity: 1;
}
}
.fade-in {
animation: fadeIn 1s ease-in-out;
}
</style>
""", unsafe_allow_html=True)
# Image display function with animation
def display_image_grid(image_paths, rows=2, cols=3, figsize=(10, 7)):
fig = plt.figure(figsize=figsize)
max_images = rows * cols
image_paths = image_paths[:max_images]
for idx, path in enumerate(image_paths):
ax = fig.add_subplot(rows, cols, idx + 1)
img = mpimg.imread(path)
ax.imshow(img)
ax.axis('off')
filename = path.split('/')[-1]
ax.set_title(filename)
plt.tight_layout()
st.pyplot(fig)
# Image encoding function
def image_encoding(inputs):
"""Load and convert image to base64 encoding"""
with open(inputs["image_path"], "rb") as image_file:
image_base64 = base64.b64encode(image_file.read()).decode("utf-8")
return {"image": image_base64}
# Streamlit Interface for uploading images and showing results
st.header("🚗 Upload Vehicle Images for Information Extraction 🚙")
# Option to select either single or batch image upload
upload_option = st.radio("Select Upload Type", ["Single Image Upload", "Batch Images Upload"])
# Single Image Upload
if upload_option == "Single Image Upload":
st.subheader("Upload a Single Vehicle Image")
uploaded_image = st.file_uploader("Choose an Image (JPEG, PNG, GIF, BMP, etc.)", type=["jpeg", "png", "gif", "bmp", "jpg"])
if uploaded_image is not None:
# Display the uploaded image with animation
image = PILImage.open(uploaded_image)
st.image(image, caption="Uploaded Image", use_container_width=True, class_="fade-in")
# Convert the uploaded image to base64
image_path = "/tmp/uploaded_image" + os.path.splitext(uploaded_image.name)[1]
with open(image_path, "wb") as f:
f.write(uploaded_image.getbuffer())
# Add button to trigger information extraction with animated hover effect
if st.button("Extract Vehicle Information"):
# Process the image through the pipeline
output = pipeline.invoke({"image_path": image_path})
# Show the results in a user-friendly format
st.subheader("Extracted Vehicle Information", class_="fade-in")
st.json(output, class_="fade-in")
# Batch Images Upload
elif upload_option == "Batch Images Upload":
st.sidebar.header("Batch Image Upload")
batch_images = st.sidebar.file_uploader("Upload Images (JPEG, PNG, GIF, BMP, etc.)", type=["jpeg", "png", "gif", "bmp", "jpg"], accept_multiple_files=True)
if batch_images:
batch_input = [{"image_path": f"/tmp/{file.name}"} for file in batch_images]
for file in batch_images:
with open(f"/tmp/{file.name}", "wb") as f:
f.write(file.getbuffer())
# Add button to trigger batch information extraction
if st.button("Extract Vehicle Information from Batch"):
# Process the batch and display the results in a DataFrame
batch_output = pipeline.batch(batch_input)
df = pd.DataFrame(batch_output)
st.dataframe(df, use_container_width=True)
# Display batch images in a grid with animation
st.subheader("Images in Grid", class_="fade-in")
image_paths = [f"/tmp/{file.name}" for file in batch_images]
st.markdown('<div class="grid-container">', unsafe_allow_html=True)
for image_path in image_paths:
st.markdown(f'<img src="data:image/jpeg;base64,{base64.b64encode(open(image_path, "rb").read()).decode()}" class="fade-in"/>', unsafe_allow_html=True)
st.markdown('</div>', unsafe_allow_html=True)
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