import json
import os
import time
import streamlit as st
from dotenv import load_dotenv
from pinecone import Pinecone, ServerlessSpec
from transformers import AutoProcessor, CLIPModel
from PIL import Image
import torch

global processor, model

processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")

load_dotenv()

# Connect to Pinecone
pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))

# Create an index if it does not exist
index_name = "index-search"
unsplash_index = None

if not pc.has_index(index_name):
    pc.create_index(name=index_name, metric="cosine",
                    dimension=512,
                    spec=ServerlessSpec(cloud="aws", region="us-east-1"))
    # Wait for the index to be ready
    while True:
        index = pc.describe_index(index_name)
        if index.status.get("ready", False):
            unsplash_index = pc.Index(index_name)
            break
        print("Waiting for index to be ready...")
        time.sleep(1)
else:
    unsplash_index = pc.Index(index_name)

# Streamlit UI
st.title("🔍 CLIP-Powered Image Search")
st.markdown("Search images using **text** or **image**!")

# Search type selection
search_type = st.radio("Select Search Type", ["Text Search", "Image Search"], horizontal=True)

def get_text_embedding(query):
    inputs = processor(text=query, return_tensors="pt")
    text_features = model.get_text_features(**inputs)
    return text_features.detach().numpy().flatten().tolist()

def get_image_embedding(image):
    image = image.convert("RGB").resize((224, 224))
    inputs = processor(images=image, return_tensors="pt")
    image_features = model.get_image_features(**inputs)
    return image_features.detach().numpy().flatten().tolist()

if search_type == "Text Search":
    search_query = st.text_input("Enter a search query (min 3 characters)")
    if len(search_query) >= 3:
        with st.spinner("Searching images..."):
            text_embedding = get_text_embedding(search_query)
            response = unsplash_index.query(top_k=10, vector=text_embedding, namespace="image-search-dataset", include_metadata=True)
        
        # Display images in two columns
        cols = st.columns(2)
        for i, result in enumerate(response.matches):
            with cols[i % 2]:
                st.image(result.metadata["url"], caption=f"Match {i+1}")

elif search_type == "Image Search":
    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
    if uploaded_file is not None:
        image = Image.open(uploaded_file)
        st.image(image, caption="Uploaded Image")
        with st.spinner("Searching for similar images..."):
            image_embedding = get_image_embedding(image)
            response = unsplash_index.query(top_k=10, vector=image_embedding, namespace="image-search-dataset", include_metadata=True)
        
        # Display images in two columns
        cols = st.columns(2)
        for i, result in enumerate(response.matches):
            with cols[i % 2]:
                st.image(result.metadata["url"], caption=f"Match {i+1}")