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Update app.py
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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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#
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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
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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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@app.post("/predict")
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async def predict(file: UploadFile = File(...)):
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result = {labels[i]: float(probs[i]) for i in range(len(labels))}
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import os
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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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# Set Hugging Face cache to avoid permission issues
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os.environ["HF_HOME"] = "/tmp/hf_cache"
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# Load processor + model
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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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# Create 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 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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try:
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# Load image
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image = Image.open(file.file).convert("RGB")
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# Preprocess
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inputs = processor(images=image, return_tensors="pt")
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# Predict
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with torch.no_grad():
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outputs = model(**inputs)
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probs = torch.nn.functional.softmax(outputs.logits, dim=-1)[0].cpu().numpy()
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# Get labels (ensure consistent order)
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labels = list(model.config.id2label.values())
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# Fix keys: return "male" and "female" only
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result = {
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"female": float(probs[labels.index("female portrait")]),
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"male": float(probs[labels.index("male portrait")])
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}
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return JSONResponse(content=result)
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except Exception as e:
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return JSONResponse(content={"error": str(e)}, status_code=500)
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