| language: en | |
| library_name: transformers | |
| pipeline_tag: image-classification | |
| tags: | |
| - vision | |
| - cervical-cancer | |
| - diagnosis | |
| license: apache-2.0 | |
| # 🩺 MedSigLip Diagnosis Model | |
| This repository contains **MedSigLip**, a deep learning model for cervical cancer image diagnosis. | |
| It takes colposcopy images as input and predicts the most likely stage/class of the condition. | |
| --- | |
| ## 📊 Model Details | |
| - **Task:** Image Classification | |
| - **Domain:** Healthcare – Cervical Cancer Diagnosis | |
| - **Framework:** Hugging Face Transformers / PyTorch | |
| - **Author:** Khanyi Tapiwa Magagula (AI Eswatini) | |
| --- | |
| ## 🚀 Inference API | |
| Once the **Inference API** is enabled, you can run predictions without any setup. Example: | |
| ```python | |
| from huggingface_hub import InferenceClient | |
| # Replace with your repo name | |
| client = InferenceClient("KhanyiTapiwa00/medsiglip-diagnosis") | |
| # Run image classification | |
| result = client.image_classification("1_10.jpg") | |
| print(result) |