second commit

without models updating

.dockerignore

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+venv/
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.db
+*.sqlite3
+*.log
+*.env
+.DS_Store
+.env.*
+.idea/
+.vscode/
+models/__pycache__/

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.keras filter=lfs diff=lfs merge=lfs -text

.gitignore

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+venv
+__pycache__

Dockerfile

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+# Use Python 3.10 as base image
+FROM python:3.10
+
+# Set working directory
+WORKDIR /app
+
+# Copy project files to the container
+COPY . .
+
+# Upgrade pip and install dependencies
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+# Expose port (FastAPI default port)
+EXPOSE 7860
+
+# Start FastAPI server with uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import io
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+
+# --- 1. Initialize FastAPI App ---
+app = FastAPI(
+    title="X-Ray Denoising API",
+    description="An API to classify noise and denoise X-ray images.",
+    version="1.0.0",
+)
+
+# --- 2. Set up CORS ---
+origins = [
+    "http://localhost:5173",
+    "http://localhost:3000",
+    "https://santy171710--classifier.hf.space"
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# --- 3. Load AI Models ---
+def load_all_models():
+    """Loads the classifier and all denoising models."""
+    print("Loading AI models...")
+    try:
+        classifier_model = tf.keras.models.load_model('models/xray_noise_classifier_resnet50v2.keras')
+        denoiser_models = {
+            'gaussian': tf.keras.models.load_model('models/gaussian_denoiser_final_model.keras'),
+            'poisson': tf.keras.models.load_model('models/poisson_denoising.keras'),
+            'salt_pepper': tf.keras.models.load_model('models/salt_pepper_denoiser.keras'),
+            'speckle': tf.keras.models.load_model('models/speckle_denoising_final_model.keras')
+        }
+        print("✅ Models loaded successfully!")
+        return classifier_model, denoiser_models
+    except Exception as e:
+        print(f"❌ Error loading models: {e}")
+        return None, None
+
+CLASSIFIER, DENOISERS = load_all_models()
+NOISE_CLASSES = ['gaussian', 'poisson', 'salt_pepper', 'speckle']
+
+# --- 4. Define Helper Functions ---
+def preprocess_image(image_bytes: bytes):
+    """Converts image bytes to a NumPy array for the models."""
+    try:
+        img = Image.open(io.BytesIO(image_bytes))
+        
+        # --- THIS IS THE CRUCIAL FIX ---
+        # 1. Convert to RGB for 3 color channels.
+        img = img.convert('RGB')
+        # 2. Resize to 224x224, the exact size the ResNet50 model expects.
+        img = img.resize((224, 224))
+        
+        img_array = np.array(img)
+        # Add the batch dimension. The channel dimension is now 3.
+        img_array = img_array[np.newaxis, ...] 
+        return img_array / 255.0
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Invalid image file. Could not preprocess. Error: {e}")
+
+def postprocess_output(denoised_array: np.ndarray):
+    """Converts model output array back to an image file in memory."""
+    # Squeeze the array to remove the batch dimension
+    processed_array = np.squeeze(denoised_array)
+    # Denormalize from 0-1 to 0-255 and convert to integer type
+    processed_array = (processed_array * 255).astype(np.uint8)
+    image = Image.fromarray(processed_array)
+    
+    img_io = io.BytesIO()
+    image.save(img_io, 'PNG')
+    img_io.seek(0)
+    return img_io
+
+# --- 5. Create the API Endpoint ---
+@app.post("/api/denoise", response_class=StreamingResponse)
+async def denoise_image(image: UploadFile = File(...)):
+    """
+    Receives an X-ray image, classifies the noise, applies the correct
+    denoiser model, and returns the cleaned image.
+    """
+    if not CLASSIFIER or not DENOISERS:
+        raise HTTPException(status_code=503, detail="Models are not available on the server.")
+
+    image_bytes = await image.read()
+
+    try:
+        # Preprocess for the classifier
+        classifier_input = preprocess_image(image_bytes)
+        
+        # Run the classifier
+        prediction = CLASSIFIER.predict(classifier_input)
+        noise_type_index = np.argmax(prediction)
+        noise_type = NOISE_CLASSES[noise_type_index]
+        print(f"Detected noise type: {noise_type}")
+
+        # --- IMPORTANT ---
+        # We need to re-process the image for the denoiser models if they
+        # expect a different input size or format (e.g., grayscale 256x256).
+        # Assuming denoisers expect grayscale 256x256 for this example.
+        img_for_denoiser = Image.open(io.BytesIO(image_bytes)).convert('L').resize((256, 256))
+        denoiser_input = np.array(img_for_denoiser)[np.newaxis, ..., np.newaxis] / 255.0
+
+        # Select and run the correct denoiser
+        denoiser_model = DENOISERS[noise_type]
+        denoised_array = denoiser_model.predict(denoiser_input)
+
+        output_image_buffer = postprocess_output(denoised_array)
+
+        return StreamingResponse(output_image_buffer, media_type="image/png")
+
+    except HTTPException as e:
+        raise e
+    except Exception as e:
+        print(f"An unexpected error occurred during processing: {e}")
+        raise HTTPException(status_code=500, detail=f"An internal error occurred: {e}")
+
+# --- 6. Add a root endpoint for basic health check ---
+@app.get("/")
+def read_root():
+    return {"status": "ok", "message": "Welcome to the X-Ray Denoising API!"}

requirements.txt

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+fastapi
+uvicorn
+python-multipart
+tensorflow==2.19.0
+numpy
+Pillow