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Browse files- Dockerfile +10 -0
- README.md +13 -5
- app.py +45 -0
- requirements.txt +6 -0
Dockerfile
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FROM python:3.10-slim
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WORKDIR /code
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COPY requirements.txt ./
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RUN pip install --no-cache-dir -r requirements.txt
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COPY . .
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
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README.md
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---
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title: Realistic Gender
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emoji:
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colorFrom:
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colorTo:
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sdk: docker
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pinned: false
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---
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-
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---
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title: Realistic Gender Classification API
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emoji: 🖼️
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colorFrom: pink
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colorTo: purple
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sdk: docker
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app_file: app.py
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pinned: false
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---
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# Realistic Gender Classification API
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This is a FastAPI service that uses [prithivMLmods/Realistic-Gender-Classification](https://huggingface.co/prithivMLmods/Realistic-Gender-Classification) to classify gender from images.
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## Endpoints
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- `/` → Upload form (HTML)
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- `/predict` → POST an image and get gender probabilities (JSON)
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app.py
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import os
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os.environ["HF_HOME"] = "/tmp/hf_cache"
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os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache"
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import io
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import torch
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from fastapi import FastAPI, File, UploadFile
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from fastapi.responses import JSONResponse, HTMLResponse
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from transformers import AutoImageProcessor, AutoModelForImageClassification
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from PIL import Image
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# Load model and processor
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processor = AutoImageProcessor.from_pretrained("prithivMLmods/Realistic-Gender-Classification")
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model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Realistic-Gender-Classification")
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# FastAPI app
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app = FastAPI()
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@app.get("/", response_class=HTMLResponse)
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async def home():
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return '''
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<html>
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<body>
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<h2>Upload an Image for Gender Detection</h2>
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<form action="/predict" enctype="multipart/form-data" method="post">
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<input name="file" type="file" accept="image/*">
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<input type="submit" value="Upload">
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</form>
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</body>
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</html>
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'''
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@app.post("/predict")
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async def predict(file: UploadFile = File(...)):
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image = Image.open(io.BytesIO(await file.read())).convert("RGB")
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inputs = processor(images=image, return_tensors="pt")
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with torch.no_grad():
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logits = model(**inputs).logits
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probs = torch.nn.functional.softmax(logits, dim=-1).cpu().numpy()[0]
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labels = model.config.id2label
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result = {labels[i]: float(probs[i]) for i in range(len(labels))}
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return JSONResponse(content=result)
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requirements.txt
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fastapi
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uvicorn
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transformers
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torch
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pillow
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python-multipart
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