add readme

README.md

@@ -1,65 +1,20 @@
 
-### hugging face
-
-- repo (lfs)  https://huggingface.co/afscomercial/dermatologic
-- project folder: models
-
-### Clone the Repository
-
-1. Navigate to the desired parent directory:
-```sh
-    cd /path/to/parent/directory
-```
-
-2. Clone the repository using Git:
-```sh
-    git clone https://huggingface.co/afscomercial/dermatologic
-```
-
-3. Navigate into the cloned repository:
-```sh
-    cd dermatologic
-```
-
-### hugging face commands
-```sh
-    huggingface-cli version
-```
-```sh
-   huggingface-cli whoami
-```
-```sh
-    huggingface-cli logout
-```
-
-> **Note:** Ensure you have the `huggingface-cli` installed and you are logged in to your Hugging Face account before running these commands. Parent folder is a git repository and the Models subdirectory is a huggingface lfs repository.
-
-
-### Setup Virtual Environment and Install Dependencies
-
-1. Create a virtual environment:
-```sh
-    python -m venv env
-```
-
-2. Activate the virtual environment:
-    - On Windows:
-```sh
-    .\env\Scripts\activate
-```
-    - On macOS and Linux:
-```sh
-    source env/bin/activate
-```
-
-3. Install `huggingface_hub`:
-```sh
-    pip install huggingface_hub
-```
-
-4. Verify the installation:
- ```sh
-    huggingface-cli version
-```
-
-> **Note:** Ensure you have Python installed on your system before creating a virtual environment.
+---
+license: mit
+tags:
+  - image-classification
+  - resnet50
+task:
+  - image-classification
+---
+# Model Card for Your Model
+
+This is a pre-trained ResNet-50 model for image classification. It has been trained on [your dataset description].
+
+## Model Usage
+You can use this model with the Hugging Face API as follows:
+```python
+from transformers import pipeline
+classifier = pipeline("image-classification", model="username/model_name")
+result = classifier("path_to_image.jpg")
+print(result)