new

app.py

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+import streamlit as st
+import requests
+from PIL import Image
+import io
+
+# ✅ Backend API URL (Replace with your actual API URL)
+API_URL = "http://127.0.0.1:8000"  # Change if running on a different server
+
+# ✅ Set Streamlit Page Config
+st.set_page_config(page_title="🔍 CLIP Image & Text Search", layout="wide")
+
+# ✅ Sidebar for Search Options
+st.sidebar.title("🔍 Search Options")
+top_k = st.sidebar.slider("🔢 Number of Similar Images", 1, 20, 10)
+
+# 📌 **Option 1: Text-to-Image Search**
+st.sidebar.subheader("📝 Search by Text")
+search_query = st.sidebar.text_input("Enter a description (e.g., 'a cute cat', 'a red car')")
+text_search_btn = st.sidebar.button("🔍 Search by Text")
+
+# 📌 **Option 2: Image-to-Image Search**
+st.sidebar.subheader("🖼️ Search by Image")
+uploaded_file = st.sidebar.file_uploader("Upload an image...", type=["jpg", "png", "jpeg"])
+image_search_btn = st.sidebar.button("🔍 Search by Image")
+
+# ✅ Process Text Search
+if search_query and text_search_btn:
+    st.subheader("🔎 Searching for similar images...")
+    
+    with st.spinner("Fetching results..."):
+        response = requests.post(f"{API_URL}/search/text", json={"query": search_query})
+
+    if response.status_code == 200:
+        results = response.json().get("results", [])
+        if results:
+            cols = st.columns(3)  # Arrange in 3 columns
+            for i, match in enumerate(results):
+                photo_id = match.get("id", "Unknown ID")
+                url = match.get("metadata", {}).get("url", None)
+                cosine_distance = 1 - match.get("score", 0)  # Convert similarity score to cosine distance
+
+                with cols[i % 3]:  # Alternate images in columns
+                    st.write(f"📷 **Photo ID**: {photo_id} | 📏 **Cosine Distance**: {cosine_distance:.4f}")
+                    if url:
+                        st.image(url, caption=f"Photo ID: {photo_id}", use_container_width=True)
+                    else:
+                        st.warning(f"⚠️ No image URL found for Photo ID: {photo_id}")
+        else:
+            st.warning("⚠️ No similar images found!")
+    else:
+        st.error("🚨 Error fetching search results.")
+
+# ✅ Process Image Search
+if uploaded_file and image_search_btn:
+    st.subheader("🔎 Searching for similar images...")
+    
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_container_width=True)
+
+    with st.spinner("Fetching results..."):
+        files = {"file": uploaded_file.getvalue()}
+        response = requests.post(f"{API_URL}/search/image", files=files)
+
+    if response.status_code == 200:
+        results = response.json().get("results", [])
+        if results:
+            cols = st.columns(3)  # Arrange in 3 columns
+            for i, match in enumerate(results):
+                photo_id = match.get("id", "Unknown ID")
+                url = match.get("metadata", {}).get("url", None)
+                cosine_distance = 1 - match.get("score", 0)  # Convert similarity score to cosine distance
+
+                with cols[i % 3]:  # Alternate images in columns
+                    st.write(f"📷 **Photo ID**: {photo_id} | 📏 **Cosine Distance**: {cosine_distance:.4f}")
+                    if url:
+                        st.image(url, caption=f"Photo ID: {photo_id}", use_container_width=True)
+                    else:
+                        st.warning(f"⚠️ No image URL found for Photo ID: {photo_id}")
+        else:
+            st.warning("⚠️ No similar images found!")
+    else:
+        st.error("🚨 Error fetching search results.")
+
+

requirements.txt

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+fastapi
+uvicorn
+streamlit
+requests
+pillow
+torch
+transformers
+numpy
+pinecone
+python-multipart