Delete app.py

app.py

@@ -1,151 +0,0 @@
-import gradio as gr
-from transformers import ViTImageProcessor, ViTForImageClassification
-from transformers import AutoImageProcessor, SiglipForImageClassification
-from PIL import Image
-import torch
-from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import JSONResponse
-from fastapi.middleware.cors import CORSMiddleware
-import uvicorn
-import io
-import logging
-
-# ----------------- LOGGER SETUP -----------------
-logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
-logger = logging.getLogger("face-analysis")
-
-# ----------------- LOAD MODELS -----------------
-# Emotion model
-emotion_processor = ViTImageProcessor.from_pretrained("abhilash88/face-emotion-detection")
-emotion_model = ViTForImageClassification.from_pretrained("abhilash88/face-emotion-detection")
-
-# Age model
-age_model_name = "prithivMLmods/facial-age-detection"
-age_model = SiglipForImageClassification.from_pretrained(age_model_name)
-age_processor = AutoImageProcessor.from_pretrained(age_model_name)
-
-# Emotion classes
-emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]
-
-# Age labels
-id2label = {
-    "0": "age 01-10",
-    "1": "age 11-20",
-    "2": "age 21-30",
-    "3": "age 31-40",
-    "4": "age 41-55",
-    "5": "age 56-65",
-    "6": "age 66-80",
-    "7": "age 80+"
-}
-
-# ----------------- PREDICT FUNCTIONS -----------------
-def predict_emotion(image: Image.Image):
-    try:
-        inputs = emotion_processor(image.convert("RGB"), return_tensors="pt")
-        with torch.no_grad():
-            outputs = emotion_model(**inputs)
-            probs = torch.nn.functional.softmax(outputs.logits, dim=-1)[0]
-        idx = torch.argmax(probs).item()
-
-        result = {
-            "predicted_emotion": emotions[idx],
-            "confidence": round(probs[idx].item(), 4),
-            "all_confidences": {emotions[i]: float(probs[i]) for i in range(len(emotions))}
-        }
-
-        logger.info(f"Predicted Emotion: {result['predicted_emotion']} (Confidence: {result['confidence']})")
-        return result
-    except Exception as e:
-        logger.error(f"Emotion prediction error: {e}")
-        return {"error": str(e)}
-
-def predict_age(image: Image.Image):
-    try:
-        inputs = age_processor(images=image.convert("RGB"), return_tensors="pt")
-        with torch.no_grad():
-            outputs = age_model(**inputs)
-            probs = torch.nn.functional.softmax(outputs.logits, dim=1).squeeze().tolist()
-        prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))}
-        idx = int(torch.argmax(torch.tensor(probs)))
-
-        result = {
-            "predicted_age": id2label[str(idx)],
-            "confidence": round(probs[idx], 4),
-            "all_confidences": prediction
-        }
-
-        logger.info(f"Predicted Age Group: {result['predicted_age']} (Confidence: {result['confidence']})")
-        return result
-    except Exception as e:
-        logger.error(f"Age prediction error: {e}")
-        return {"error": str(e)}
-
-# ----------------- FASTAPI APP -----------------
-app = FastAPI()
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-@app.get("/health")
-async def health():
-    return {"status": "ok"}
-
-@app.post("/predict")
-async def predict(file: UploadFile = File(...)):
-    try:
-        contents = await file.read()
-        image = Image.open(io.BytesIO(contents))
-
-        emotion_result = predict_emotion(image)
-        age_result = predict_age(image)
-
-        logger.info(f"API Response -> Emotion: {emotion_result.get('predicted_emotion')} | Age: {age_result.get('predicted_age')}")
-
-        return JSONResponse(content={
-            "emotion": emotion_result,
-            "age": age_result
-        })
-    except Exception as e:
-        logger.error(f"API Error: {e}")
-        return JSONResponse(content={"error": str(e)}, status_code=500)
-
-# ----------------- GRADIO DEMO -----------------
-def gradio_wrapper(image):
-    emotion_result = predict_emotion(image)
-    age_result = predict_age(image)
-
-    if "error" in emotion_result or "error" in age_result:
-        return "Error", {}, "Error", {}
-
-    return (
-        f"{emotion_result['predicted_emotion']} ({emotion_result['confidence']:.2f})",
-        emotion_result["all_confidences"],
-        f"{age_result['predicted_age']} ({age_result['confidence']:.2f})",
-        age_result["all_confidences"]
-    )
-
-demo = gr.Interface(
-    fn=gradio_wrapper,
-    inputs=gr.Image(type="pil"),
-    outputs=[
-        gr.Label(num_top_classes=1, label="Top Emotion"),
-        gr.Label(label="Emotion Probabilities"),
-        gr.Label(num_top_classes=1, label="Top Age Group"),
-        gr.Label(label="Age Probabilities"),
-    ],
-    title="Face Emotion + Age Detection",
-    description="Upload a face image and detect both emotion (Angry, Happy, etc.) and estimated age group (01–10, 11–20, ... 80+)."
-)
-
-# Mount Gradio at /gradio
-app = gr.mount_gradio_app(app, demo, path="/gradio")
-
-# ----------------- RUN -----------------
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)