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app.py

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+import streamlit as st
+import requests
+from PIL import Image, UnidentifiedImageError
+import io
+import numpy as np
+from transformers import AutoProcessor, CLIPModel
+import logging
+from pinecone import Pinecone
+import streamlit as st
+
+# Function to check authentication
+def check_authentication():
+    st.sidebar.title("🔑 Login")
+    username = st.sidebar.text_input("Username", key="username_input")
+    password = st.sidebar.text_input("Password", type="password", key="password_input")
+    login_button = st.sidebar.button("Login")
+
+    if login_button:
+        try:
+            stored_users = dict(st.secrets["credentials"])  # Convert to dictionary
+            if username in stored_users and stored_users[username] == password:
+                st.session_state["authenticated"] = True
+                st.session_state["username"] = username
+                st.rerun()  # ✅ Updated method
+            else:
+                st.sidebar.error("❌ Invalid username or password")
+        except Exception as e:
+            st.sidebar.error("⚠️ Authentication system error")
+            st.sidebar.text(str(e))
+
+# Check authentication status
+if "authenticated" not in st.session_state:
+    st.session_state["authenticated"] = False
+
+if not st.session_state["authenticated"]:
+    check_authentication()
+    st.stop()
+
+# Main app content (only accessible after authentication)
+st.write("Welcome! You are successfully authenticated.")
+
+
+# Setup logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger(__name__)
+
+# Load CLIP model and processor
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+# Initialize Pinecone
+pc = Pinecone(api_key="pcsk_6QAd2e_Js1mL941ky9vvGhkGpsGmR7H8aDjKWp2vzpMiRDSvFEFGf5VT6meRJeAft1pNaE")
+index_name = "images-index"
+
+# Ensure Pinecone index exists
+index_list = pc.list_indexes().names()
+if index_name not in index_list:
+    st.error(f"Index '{index_name}' not found. Make sure it is created.")
+    st.stop()
+
+# Initialize Pinecone index
+unsplash_index = pc.Index(index_name)
+
+def embed_text(text):
+    inputs = processor(text=text, return_tensors="pt")
+    text_features = model.get_text_features(**inputs)
+    return text_features.detach().cpu().numpy().flatten().tolist()
+
+def embed_image(image):
+    inputs = processor(images=image, return_tensors="pt")
+    image_features = model.get_image_features(**inputs)
+    return image_features.detach().cpu().numpy().flatten().tolist()
+
+# Streamlit UI
+st.title("📌Image & Text Embedding Search using CLIP and Pinecone")
+
+option = st.radio("Choose input type:", ("Text", "Image🏞️"))
+
+if option == "Text":
+    query_text = st.text_input("Enter a search term:")
+    if st.button("Embed & Search"):
+        if query_text:
+            query_embedding = embed_text(query_text)
+            search_results = unsplash_index.query(
+                vector=query_embedding,
+                top_k=10,
+                include_metadata=True,
+                namespace="image-search-dataset"
+            )
+            if search_results and "matches" in search_results:
+                st.subheader("Search Results:")
+                for match in search_results["matches"]:
+                    url = match["metadata"].get("url", "No URL")
+                    st.image(url, caption=f"Score: {match['score']:.4f}", use_container_width=True)
+            else:
+                st.warning("No matching images found.")
+        else:
+            st.error("Please enter a search term.")
+
+elif option == "Image":
+    uploaded_file = st.file_uploader("Upload an image:", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_container_width=True)
+        if st.button("Embed & Search"):
+            query_embedding = embed_image(image)
+            search_results = unsplash_index.query(
+                vector=query_embedding,
+                top_k=10,
+                include_metadata=True,
+                namespace="image-search-dataset"
+            )
+            if search_results and "matches" in search_results:
+                st.subheader("Search Results:")
+                for match in search_results["matches"]:
+                    url = match["metadata"].get("url", "No URL")
+                    st.image(url, caption=f"Score: {match['score']:.4f}", use_container_width=True)
+            else:
+                st.warning("No similar images found.")

requirements.txt

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+streamlit
+requests
+pillow
+numpy
+transformers
+torch
+pinecone