added app file

src/app.py

@@ -0,0 +1,91 @@
+import os
+import time
+import logging
+import streamlit as st
+import requests
+import torch
+from dotenv import load_dotenv
+from pinecone import Pinecone, ServerlessSpec
+from transformers import AutoTokenizer, CLIPModel, AutoProcessor
+from PIL import Image
+
+# Logging setup
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger(__name__)
+
+# Load environment variables
+load_dotenv()
+PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
+# HF_ACCESS_TOKEN = os.getenv("HF_ACCESS_TOKEN")
+
+# # Ensure Hugging Face authentication
+# from huggingface_hub import login
+# login(HF_ACCESS_TOKEN)
+
+# Load CLIP model and processor
+tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-large-patch14")
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+# Connect to Pinecone
+pc = Pinecone(api_key=PINECONE_API_KEY)
+
+# Ensure the index exists
+index_name = "index-search"
+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"))
+    time.sleep(5)  # Wait for index to initialize
+
+unsplash_index = pc.Index(index_name)
+
+# Streamlit UI
+st.title("Search Images by Text or Image")
+
+search_mode = st.radio("Choose search mode:", ["Text Search", "Image Search"])
+
+if search_mode == "Text Search":
+    search_query = st.text_input("Search (at least 3 characters)")
+    if len(search_query) >= 3:
+        with st.spinner("Searching images..."):
+            inputs = tokenizer([search_query], padding=True, return_tensors="pt")
+            text_features = model.get_text_features(**inputs)
+            text_embedding = text_features.detach().numpy().flatten().tolist()
+
+            response = unsplash_index.query(
+                top_k=10,
+                vector=text_embedding,
+                namespace="image-search-dataset",
+                include_metadata=True
+            )
+
+        # Display results
+        cols = st.columns(2)
+        for i, result in enumerate(response.matches):
+            with cols[i % 2]:
+                st.image(result.metadata["url"], caption=f"Score: {result.score:.4f}")
+
+elif search_mode == "Image Search":
+    uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+    if uploaded_file:
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        with st.spinner("Searching similar images..."):
+            inputs = processor(images=image, return_tensors="pt")
+            image_features = model.get_image_features(**inputs)
+            image_embedding = image_features.detach().numpy().flatten().tolist()
+
+            response = unsplash_index.query(
+                top_k=10,
+                vector=image_embedding,
+                namespace="image-search-dataset",
+                include_metadata=True
+            )
+
+        # Display results
+        cols = st.columns(2)
+        for i, result in enumerate(response.matches):
+            with cols[i % 2]:
+                st.image(result.metadata["url"], caption=f"Score: {result.score:.4f}")