Update README.md

README.md

@@ -12,11 +12,15 @@ co2_eq_emissions:
   emissions: 0.68402736720358
 ---
 
-# Model Trained Using AutoTrain
+# Fake News Classification
+
+This is a Food Category Image Classifier model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to recognize 11 different categories of foods, including **Bread**, **Dairy Product**, **Dessert**, **Egg**, **Fried Food**, **Meat**, **Noodles-Pasta**, **Rice**, **Seafood**, **Soup**, and **Vegetable-Fruit**. It can accurately classify an image of food into one of these categories by analyzing its visual features. This model can be used by food bloggers, restaurants, and recipe websites to quickly categorize and sort their food images, making it easier to manage their content and provide a better user experience.
+
+### Gradio
+
+Tis model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the data-food-classification model:
+[![Open In HF Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Kaludi/Food-Category-Classification_App)
 
-- Problem type: Binary Classification
-- Model ID: 93303145819
-- CO2 Emissions (in grams): 0.6840
 
 ## Validation Metrics
 
@@ -25,26 +29,4 @@ co2_eq_emissions:
 - Precision: 0.985
 - Recall: 1.000
 - AUC: 0.992
-- F1: 0.992
-
-## Usage
-
-You can use cURL to access this model:
-
-```
-$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Kaludi/autotrain-test13-93303145819
-```
-
-Or Python API:
-
-```
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-model = AutoModelForSequenceClassification.from_pretrained("Kaludi/autotrain-test13-93303145819", use_auth_token=True)
-
-tokenizer = AutoTokenizer.from_pretrained("Kaludi/autotrain-test13-93303145819", use_auth_token=True)
-
-inputs = tokenizer("I love AutoTrain", return_tensors="pt")
-
-outputs = model(**inputs)
-```
+- F1: 0.992