changes

app.py

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+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.")

requirements.txt

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+Streamlit
+GenerativeAI
+PIL
+numpy
+requests
+tensorflow
+scikit-learn
+io
+os