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README.md

@@ -5,18 +5,22 @@ This project is divided into four main modules, each focusing on a distinct aspe
 ## Module 1: Data Preparation and Backend Setup
 
 ### Task 1: E-commerce Dataset Cleaning
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 - *Objective*: Ensure the dataset is clean and ready for analysis and vectorization.
 - *Key Actions*: Remove duplicates, handle missing values, and standardize formats.
 
 ### Task 2: Vector Database Creation
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 - *Objective*: Set up a vector database using Pinecone to store product vectors.
 - *Key Actions*: Define the database schema and integrate with Pinecone.
 
 ### Task 3: Similarity Metrics Selection
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 - *Objective*: Choose and justify the similarity metrics used to compare product vectors.
 - *Key Actions*: Evaluate different metrics (e.g., cosine similarity, dot product) and select the best fit based on the dataset characteristics.
 
 ### Endpoint 1: Product Recommendation Service
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 - *Functionality*: Handle natural language queries to recommend products, including safeguards against bad queries and sensitive data exposure.
 - *Input*: Customer's natural language query.
 - *Output*: Product matches array and a natural language response within specified constraints.
@@ -24,14 +28,17 @@ This project is divided into four main modules, each focusing on a distinct aspe
 ## Module 2: OCR and Web Scraping
 
 ### Task 4: OCR Functionality Implementation
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 - *Objective*: Develop the capability to extract text from images using OCR technology.
 - *Key Actions*: Integrate and configure an OCR tool (e.g., Tesseract).
 
 ### Task 5: Web Scraping for Product Images
-- *Objective*: Scrape product images from e-commerce websites for training data.
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-- *Key Actions*: Automate scraping, download images, and store them systematically.
+- *Objective*: Scrape product images from e-commerce websites for training data ``CNN_Model_Train_Data.csv``.
+- *Key Actions*: Automate scraping, download images, and store them systematically and make sure you have enough data to train the CNN model.
 
 ### Endpoint 2: OCR-Based Query Processing
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 - *Functionality*: Extract and process handwritten queries using the same logic as Endpoint 1.
 - *Input*: Image file with handwritten text.
 - *Output*: Same output format as Endpoint 1, adapted for image inputs.
@@ -39,10 +46,12 @@ This project is divided into four main modules, each focusing on a distinct aspe
 ## Module 3: CNN Model Development
 
 ### Task 6: CNN Model Training
-- *Objective*: Develop a CNN model from scratch to identify products from images.
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+- *Objective*: Develop a CNN model from scratch using only the ``products`` mentioned on ``CNN_Model_Train_Data.csv`` to identify products from images.
 - *Key Actions*: Train the model using scraped images and clean data without using pre-trained models.
 
 ### Endpoint 3: Image-Based Product Detection
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 - *Functionality*: Use the CNN model to identify products from images and match them using the vector database.
 - *Input*: Product image.
 - *Output*: Product description and matching products in a format consistent with other endpoints.
@@ -50,12 +59,15 @@ This project is divided into four main modules, each focusing on a distinct aspe
 ## Module 4: Frontend Development and Integration
 
 ### Frontend Page 1: Text Query Interface
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 - *Features*: Form to submit text queries, display natural language responses, and a product details table.
 
 ### Frontend Page 2: Image Query Interface
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 - *Features*: Allows users to upload images of handwritten queries and displays results similar to Page 1.
 
 ### Frontend Page 3: Product Image Upload Interface
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 - *Features*: Users can upload product images, and view the identified product description and related products in natural language and tabular format.
 
 ## Instructions for Presentation
@@ -101,12 +113,15 @@ Participants are required to create two sets of videos for each module, detailin
 ## Instructions for Coding
 
 ### General Guidelines
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 - *Class-Based Implementation*: It is recommended to use class-based implementation for all backend services to ensure organized, reusable, and maintainable code.
 - *Best Practices*:
   - *ACID Properties*: Ensure that database transactions are Atomic, Consistent, Isolated, and Durable to maintain data integrity and reliability.
   - *Modularity*: Build the codebase with clear modularity in mind. Separate different functionalities into distinct modules to enhance readability and maintainability.
 - *Packaging*: Organize your code into packages that reflect the services they provide. This approach not only helps in maintaining the code but also simplifies the deployment and scaling process.
+- Directories: Whenever you will test on notebook make sure you keep all the notebooks in ``notebook`` directory and use proper naming for the notebooks.
 
 ### Tech Stack
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 - *Web Framework*: Use Flask for developing the backend. Flask provides flexibility and ease of use for setting up API services.
 - *Vector Database*: Integrate Pinecone to manage and query vector data efficiently. Pinecone supports scalable vector searches which are crucial for the recommendation systems in this project.