File size: 3,585 Bytes
23f0310 |
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 |
import os
import streamlit as st
import requests
from PIL import Image
from dotenv import load_dotenv
import google.generativeai as genai
# Load environment variables
load_dotenv()
api_key = os.getenv("GEMINI_API_KEY")
model = genai.GenerativeModel("gemini-1.5-flash")
# Fetch analysis from Gemini API
def get_gemini_response(prompt, image):
try:
return model.generate_content([prompt, image]).text
except Exception as e:
st.error(f"Gemini API error: {e}")
return None
# Fetch artwork info from MET Museum
def fetch_met_data(object_id):
try:
url = f"https://collectionapi.metmuseum.org/public/collection/v1/objects/{object_id}"
response = requests.get(url)
return response.json() if response.status_code == 200 else None
except Exception as e:
st.error(f"Error fetching MET data: {e}")
return None
# --- Streamlit App ---
st.set_page_config(layout="wide")
st.title('🎨 AI-Assisted Art Authenticity and Style Analysis')
st.write("Upload an artwork image to analyze its authenticity, brushstroke, color palette, and composition.")
uploaded_image = st.file_uploader("Upload Artwork", type=["jpg", "jpeg", "png"])
if uploaded_image:
image = Image.open(uploaded_image).resize((200, 200)) # Resize for consistency
# MET artwork example
met_id = 436121
met_data = fetch_met_data(met_id)
met_image = None
if met_data and met_data.get("primaryImage"):
met_image = Image.open(requests.get(met_data['primaryImage'], stream=True).raw).resize((512, 512))
col1, col2 = st.columns(2)
with col1:
st.image(image, caption="📥 Uploaded Artwork", use_container_width=True)
with col2:
if met_image:
st.image(met_image, caption=f"🖼️ {met_data['title']}", use_container_width=True)
st.markdown(f"**👨🎨 Artist:** {met_data['artistDisplayName']}")
st.markdown(f"**📅 Year:** {met_data['objectDate']}")
st.markdown(f"**🎨 Medium:** {met_data['medium']}")
else:
st.warning("Could not load MET comparison image.")
st.divider()
st.markdown("## 🧠 Comparative Art Analysis")
prompt_uploaded = (
"Analyze this artwork. Focus on: "
"1. Brushstroke technique, 2. Color palette, 3. Composition style and structure. "
"Identify any stylistic clues for authenticity or imitation."
)
prompt_met = (
"Analyze this famous artwork from the MET collection. Focus on: "
"1. Brushstroke technique, 2. Color palette, 3. Composition style and structure."
)
col3, col4 = st.columns(2)
with col3:
st.markdown("### 🎨 Uploaded Artwork")
analysis_uploaded = get_gemini_response(prompt_uploaded, image)
st.write(analysis_uploaded or "No analysis returned.")
with col4:
st.markdown("### 🖼️ MET Artwork")
if met_image:
analysis_met = get_gemini_response(prompt_met, met_image)
st.write(analysis_met or "No analysis returned.")
st.divider()
st.subheader("📝 Expert Feedback")
# Feedback form
with st.form("feedback_form"):
feedback_text = st.text_area("Your feedback on the analysis:")
submitted = st.form_submit_button("Send")
if submitted and feedback_text.strip():
st.success("Thanks for your feedback!")
st.markdown(f"> {feedback_text}")
# Force a rerun to clear the feedback
st.rerun()
else:
st.info("Please upload an image to begin analysis.")
|