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README.md

@@ -1,11 +1,26 @@
----
-title: Gender Api Fastapi
-emoji: 🌍
-colorFrom: blue
-colorTo: indigo
-sdk: docker
-pinned: false
-short_description: 'API gender '
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Gender Classification API (Docker)
+
+This is a Hugging Face Space API using FastAPI + Docker to classify gender from an image using `prithivMLmods/Gender-Classifier-Mini`.
+
+## Usage
+
+POST to:
+
+```
+https://benstaf-gender-api-fastapi.hf.space/classify/
+```
+
+## Example (Python)
+
+```python
+import requests
+
+with open("face.jpg", "rb") as f:
+    r = requests.post("https://benstaf-gender-api-fastapi.hf.space/classify/", files={"image": f})
+    print(r.json())
+```
+
+## Deployment
+
+- SDK: Docker
+- Port: 7860 (required)

app.py

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+from fastapi import FastAPI, UploadFile, File
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+from PIL import Image
+import torch.nn.functional as F
+import torch
+import io
+
+app = FastAPI()
+
+model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Gender-Classifier-Mini")
+processor = AutoImageProcessor.from_pretrained("prithivMLmods/Gender-Classifier-Mini")
+
+@app.post("/classify/")
+async def classify_gender(image: UploadFile = File(...)):
+    contents = await image.read()
+    img = Image.open(io.BytesIO(contents)).convert("RGB")
+    inputs = processor(images=img, return_tensors="pt")
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = F.softmax(logits, dim=1)
+        pred = torch.argmax(probs).item()
+        confidence = probs[0][pred].item()
+        label = model.config.id2label[pred]
+
+    return {"label": label, "confidence": confidence}

requirements.txt

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+fastapi
+uvicorn
+transformers
+torch
+pillow