app.py

import streamlit as st
import requests
from PIL import Image
import io

# ✅ Backend API URL (Replace with your actual API URL)
API_URL = "http://0.0.0.0:7860"  # 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.")