Update README.md

README.md

@@ -1,25 +1,43 @@
 ---
 title: Generate With OpenAI CLIP
-emoji: 🐢
+emoji: 🎨🤖🖼️
 colorFrom: red
 colorTo: indigo
 sdk: streamlit
-sdk_version: 1.29.0
+sdk_version: 1.37.1
 app_file: app.py
 pinned: false
 license: afl-3.0
 ---
 
-# Quest 1 - Generate with OpenAI CLIP
-**Created by:** *Lightning.ai*
+# Image Understanding Model 🎨🤖
 
+This application leverages OpenAI's CLIP (Contrastive Language-Image Pretraining) model to analyze images and match them with the most accurate text descriptions provided by the user. 
+It uses Streamlit to create an interactive web interface where users can upload images and input descriptions. 
+The model then predicts which description best fits the image based on probabilities.
 
-**Objective:** Use Open AI's CLIP model to annotate images.
+## Features
 
-- Learn how to make generation faster using GPU's.
-- GPUs can run ~100x faster than CPUs, and in some cases even more. To get more speed we can even run on multiple GPUs.
-- Experimented with  A10G GPU on Lightning Studio
+- **Image Upload**: Users can upload an image (JPG, PNG, or JPEG).
+- **Description Input**: Users input 3 descriptions about the image (e.g., 2 false and 1 true).
+- **Prediction**: The model predicts the most likely description out of the three and provides a confidence score.
+- **Progress Bar**: A visual progress bar displays the confidence of the best description.
+
+## How It Works
+
+1. **Upload an Image**: Users upload an image of their choice.
+2. **Input Descriptions**: Users are prompted to enter 3 descriptions about the image, with 1 description being true.
+3. **Model Prediction**: Once the descriptions are submitted, the CLIP model evaluates the image and the provided descriptions to predict which description best matches the image.
+4. **Result Display**: The app displays the best-matching description and its corresponding probability, along with a progress bar showing the confidence of the prediction.
+
+## Technology Stack
+
+- **OpenAI CLIP**: The core model used for image and text understanding.
+- **Torch**: Used for model inference and handling tensors.
+- **Streamlit**: Provides the interactive web interface for uploading images and entering descriptions.
+- **Pillow**: For handling image processing.
+- **NumPy**: For efficient array and matrix operations.
 
 ## APPLICATION
 
-Simple Streamlit app for playing '2 Lies and a truth' with the model and friends.
+Simple Streamlit app for playing '2 Lies and a truth' with the model and friends.