Started adding api's

README.md

@@ -9,4 +9,64 @@ app_file: src/app/app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<!-- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+📌 Project Overview
+
+Look-A-Like Image Finder is an AI-powered image search tool that allows users to find visually similar images based on an input image or a text query. It utilizes Pinecone for vector search, OpenAI CLIP for embeddings, and Streamlit for an intuitive user interface.
+
+✨ Features
+🔍 Image Search – Find images similar to a given input.
+📝 Text-to-Image Search – Retrieve images using natural language descriptions.
+📂 Efficient Indexing – Uses Pinecone to store and retrieve image embeddings.
+
+🛠 Tech Stack
+🖼 Model – OpenAI CLIP (Contrastive Language-Image Pretraining)
+🔍 Vector Search – Pinecone (Efficient indexing and retrieval)
+🖥 Frontend – Streamlit (Interactive web UI)
+🐍 Backend – Python
+☁ Deployment – Hugging Face Spaces
+
+📥 Resources
+🔗 Download the Dataset : {https://unsplash.com/data/lite/latest}
+🔗 Pinecone API Key Setup : {https://www.pinecone.io/} -->
+
+<!-- 🚀 How to Run the Application Locally -->
+
+<!-- Step 1: Clone the Repository -->
+<!-- Copy and paste the following command into your terminal -->
+<!-- git clone https://github.com/Vela-Test1993/lookalike-image-finder.git -->
+<!-- cd lookalike-image-finder -->
+
+<!-- Step 2: Install Dependencies -->
+<!-- Run the following command to install the required packages -->
+<!-- pip install -r requirements.txt -->
+
+<!-- Step 3: Start the Application -->
+<!-- Use the command below to launch the app -->
+<!-- streamlit run src/app/app.py -->
+
+<!-- Step 4: Open in Your Browser -->
+<!-- The application will automatically open in your default web browser. -->
+
+<!-- ⚠ Important: API Key Setup -->
+<!-- Create a .env file in the project's root directory and store your Pinecone API key inside it. -->
+
+
+<!-- 🔑 Setting Up the Application Components -->
+
+<!-- 🟢 Pinecone: Vector Database -->
+<!-- 1️⃣ Log in to your Pinecone account. -->
+<!-- 2️⃣ Retrieve your Pinecone API key. -->
+<!-- 3️⃣ Store the API key securely in the .env file. -->
+
+<!-- 📂 Dataset: Image Processing -->
+<!-- 1️⃣ Download the dataset from the following link: https://unsplash.com/data/lite/latest -->
+<!-- 2️⃣ Convert the images into vector embeddings using OpenAI CLIP. -->
+<!-- 3️⃣ Store the embeddings in Pinecone for efficient retrieval. -->
+
+<!-- 🎨 Streamlit: Web Interface -->
+<!-- 1️⃣ Streamlit is used to build the user-friendly UI/UX for the application. -->
+<!-- 2️⃣ The frontend allows users to search for similar images using image or text queries. -->
+<!-- 3️⃣ The interface is interactive and easy to navigate. -->
+

src/api/main.py

@@ -0,0 +1,7 @@
+from fastapi import FastAPI, HTTPException
+from routes import homepage_router
+
+app = FastAPI()
+
+# Include the routers in the app
+app.include_router(homepage_router, prefix="/api", tags=["Home Page"])

src/api/routes/__init__.py

@@ -0,0 +1 @@
+from .home_page_api import router as homepage_router

src/api/routes/home_page_api.py

@@ -0,0 +1,16 @@
+import os
+import sys
+src_directory = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..", "src"))
+sys.path.append(src_directory)
+from fastapi import APIRouter, HTTPException
+from schemas import schema
+from app import homepage
+
+
+router = APIRouter()
+
+@router.post("/{text_querry}", summary="Find the image by text")
+def search_image(search_image: schema.ImageSearch):
+    homepage.get_images_by_text(search_image.querry_text)
+
+    

src/api/schemas/schema.py

@@ -0,0 +1,5 @@
+from pydantic import BaseModel
+
+class ImageSearch(BaseModel):
+    querry_text : str
+

src/app/app.py

@@ -1,7 +1,6 @@
 import homepage
 import torch
-import streamlit as st
 
 homepage.setup_page()
 homepage.search_tab()
-st.link_button("Navigate to load data page",url="http://localhost:8501/load_data_page")
+# st.link_button("Navigate to load data page",url="http://localhost:8501/load_data_page")

src/app/request_app.py

@@ -0,0 +1,6 @@
+import requests
+
+def get_api(end_point : str = None):
+    # r = requests.get(f"https://velatest-world-data-insights-api.hf.space/{end_point}")
+    r = requests.get(f"http://127.0.0.1:8000/{end_point}")
+    return r.json()

src/data/request_images.py

@@ -21,4 +21,4 @@ def convert_image_to_embedding_format(query_image):
         logger.info("Loaded the image to embed successfully")
         return image
     except Exception as e:
-        logger.error(f"Unable to load the image to embed {e}")
+        logger.error(f"Unable to load the image to embed {e}")