🔧 submission-base234

README.md

@@ -10,23 +10,84 @@ To get started with the oneAPI-GenAI-Hackathon-2023 repository, follow these ste
   1. Fork this repository
   2. Create a folder with your Team Name
   3. Upload all the code and necessary files in the created folder
-  4. Upload a **README.md** file in your folder with the below mentioned informations.
+  4. Upload a **README.md** file in your folder with the below-mentioned information.
   5. Generate a Pull Request with your Team Name. (Example: submission-XYZ_team)
 
 ### README.md must consist of the following information:
 
-#### Team Name -
-#### Problem Statement - 
-#### Team Leader Email -
+#### Team Name - base234
+#### Problem Statement - Revolutionary AI-Infused Retail Platform
+#### Team Leader Email - [email protected]
 
 ### A Brief of the Prototype:
-  This section must include UML Diagrams and prototype description
+  This project aims to develop a generative AI system that revolutionizes how customers interact with e-commerce platforms by introducing visual search capabilities leveraging state-of-the-art LLM technologies.
+  - This system will allow users to upload images of desired products or items, then identify similar products from the product catalog, enhancing the user's shopping experience.
+  - We develop a generative AI system that enables visual search, allowing customers to find products by uploading images rather than using text-based queries.
 
 ### Tech Stack: 
-   List Down all technologies used to Build the prototype
+   - LLM
+   - Python
+   - React
+   - Flask
+   - Intel Oneapi Developer cloud platform
 
 ### Step-by-Step Code Execution Instructions:
-  This Section must contain a set of instructions required to clone and run the prototype so that it can be tested and deeply analyzed
+  - For visual search, we’ve encoded query images using the trained encoder and then found similarly encoded representations from our dataset.
+  - OpenAI's CLIP (Contrastive Language-Image Pretraining) model is used for embedding images.
+  - Each of these embeddings is then saved to Pinecone Vector Database for semantic search against the query.
+  - For product discovery, we generate new product images using the trained generator and display them to users based on their preferences.
+
+#### Prerequisites
+1. An IDC compute instance <br>
+2. SSH access to the instance <br>
+3. Miniconda (instructions included for installation) <br>
+#### Installation and Setup
+1. SSH into the IDC Compute Instance
+2. To start, SSH into your IDC compute instance. Replace <YourInstanceIP> with your actual instance IP address.
+```
+ssh username@<YourInstanceIP>
+```
+2. Install Miniconda
+Once logged in, install Miniconda for simplified Python environment management. Run the following:
+```
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+bash Miniconda3-latest-Linux-x86_64.sh
+```
+Follow the on-screen instructions to complete the installation.
+
+3. Create and Activate a Conda Environment
+Create a new Conda environment with Python version 3.10.6:
+
+```
+conda create -n myenv python=3.10.6
+conda activate myenv
+```
+This will create and activate a new environment named myenv.
+
+4. Install Required Modules
+Install the required modules specified in requirements.txt:
+```
+pip install -r requirements.txt
+```
+Ensure requirements.txt is present in your current directory.
+
+5. Accessing React UI, Flask API, and Port Forwarding
+To access the React UI, and Flask API, use SSH tunneling for port forwarding. Run the following command on your local machine:
+```
+python app.py
+npm run dev
+```
+```
+ssh -L 8501:localhost:8501 username@<YourInstanceIP>
+```
+Then, you can access the React UI, and Flask API by navigating to localhost:8501 and localhost:8502 in your web browser.
+
 
 ### Future Scope:
-   Write about the scalability and futuristic aspects of the prototype developed
+   * [x] Image to Image Search.
+   * [x] Text to Image Search.
+   * [ ] Product Discovery (Create new types of products based on user requirements).
+   * [ ] Use generative AI to extract results from multiple E-commerce databases.
+   * [ ] Create an Android/iOS Application.
+   * [ ] Deploy the Application in AWS using EKS (Elastic Kubernetes Service)
+

Tansaku/APP_Constants.py

@@ -0,0 +1 @@
+APP_ENDPOINT = "/tansaku/api/v1"

Tansaku/Dockerfile

@@ -0,0 +1,24 @@
+FROM python:3.10.6-slim-bullseye as base
+
+
+COPY ./ml_requirements.txt /ml_requirements.txt
+RUN pip install --no-cache-dir -r ml_requirements.txt
+
+COPY ./requirements.txt /requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+
+FROM python:3.10.6-slim-bullseye as build
+
+RUN apt-get update && apt-get install -y libgl1-mesa-glx libglib2.0-0
+
+COPY --from=base /usr/local/lib/python3.10/site-packages/ /usr/local/lib/python3.10/site-packages/
+COPY --from=base /usr/local/bin/ /usr/local/bin/
+
+RUN mkdir -p /app
+COPY . /app
+WORKDIR /app/
+
+CMD ["/usr/bin/supervisord"]
+CMD ["python", "app.py"]
+ENV PYTHONBUFFERED 1