Create app.py

app.py

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+from fastapi import FastAPI, UploadFile, File, Form
+from fastapi.responses import Response
+import torch
+import torch.nn as nn
+import numpy as np
+import cv2
+from torchvision import models, transforms
+from PIL import Image
+import io
+
+app = FastAPI(title="Falcon Change Detection API", version="1.0")
+
+# ==========================================
+# 1. LOAD MODEL ON SERVER STARTUP
+# ==========================================
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Server booting on: {DEVICE}")
+
+class SiameseResNetUNet(nn.Module):
+    def __init__(self, n_classes=1):
+        super().__init__()
+        base_model = models.resnet50(weights=None)
+        self.encoder = nn.Sequential(*list(base_model.children())[:-2]) 
+        self.up1 = nn.ConvTranspose2d(2048 * 2, 512, kernel_size=2, stride=2)
+        self.conv1 = nn.Sequential(nn.Conv2d(512, 512, 3, 1, 1), nn.BatchNorm2d(512), nn.ReLU(inplace=True))
+        self.up2 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
+        self.conv2 = nn.Sequential(nn.Conv2d(256, 256, 3, 1, 1), nn.BatchNorm2d(256), nn.ReLU(inplace=True))
+        self.up3 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
+        self.conv3 = nn.Sequential(nn.Conv2d(128, 128, 3, 1, 1), nn.BatchNorm2d(128), nn.ReLU(inplace=True))
+        self.final_up = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=True)
+        self.final_conv = nn.Conv2d(128, n_classes, kernel_size=1)
+        
+    def forward(self, x1, x2):
+        f1, f2 = self.encoder(x1), self.encoder(x2)
+        x = torch.cat([f1, f2], dim=1) 
+        x = self.conv1(self.up1(x))
+        x = self.conv2(self.up2(x))
+        x = self.conv3(self.up3(x))
+        return self.final_conv(self.final_up(x))
+
+model = SiameseResNetUNet().to(DEVICE)
+# ⚠️ Ensure this matches your uploaded file name perfectly
+state_dict = torch.load("falcon_india_finetuned.pth", map_location=DEVICE)
+if list(state_dict.keys())[0].startswith('module.'):
+    state_dict = {k[7:]: v for k, v in state_dict.items()}
+model.load_state_dict(state_dict)
+model.eval()
+
+# ==========================================
+# 2. THE API ENDPOINTS
+# ==========================================
+@app.get("/")
+def health_check():
+    return {"status": "online", "model": "Falcon Siamese-UNet", "device": DEVICE}
+
+@app.post("/detect")
+async def detect_changes(
+    image_past: UploadFile = File(...), 
+    image_present: UploadFile = File(...),
+    volume_knob: float = Form(11.0),   # Default to your best value
+    threshold: float = Form(0.85)      # Default to your best value
+):
+    # 1. Read both uploaded images
+    bytes_past = await image_past.read()
+    bytes_present = await image_present.read()
+    
+    imgA_raw = Image.open(io.BytesIO(bytes_past)).convert("RGB").resize((512, 512), Image.BILINEAR)
+    imgB_raw = Image.open(io.BytesIO(bytes_present)).convert("RGB").resize((512, 512), Image.BILINEAR)
+    
+    imgB_resized = np.array(imgB_raw) # Keep a numpy copy to draw on
+    
+    # 2. Prepare for the AI
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
+    ])
+    
+    tA = transform(imgA_raw).unsqueeze(0).to(DEVICE)
+    tB = transform(imgB_raw).unsqueeze(0).to(DEVICE)
+
+    # 3. Run Inference
+    with torch.no_grad():
+        preds = model(tA, tB)
+        probs = torch.sigmoid(preds).cpu().numpy()[0][0]
+
+    # 4. The Night Vision Amplifier
+    amplified_probs = np.clip(probs * volume_knob, 0, 1)
+    binary_mask = (amplified_probs > threshold).astype(np.uint8) * 255
+
+    # 5. Draw Red Boundaries on the Present Image
+    contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    img_with_boundaries = imgB_resized.copy()
+    cv2.drawContours(img_with_boundaries, contours, -1, (255, 0, 0), 2)
+
+    # 6. Compress back to a PNG and send to the user
+    is_success, buffer = cv2.imencode(".png", cv2.cvtColor(img_with_boundaries, cv2.COLOR_RGB2BGR))
+    io_buf = io.BytesIO(buffer)
+    
+    return Response(content=io_buf.getvalue(), media_type="image/png")