fix: Ajuste no resiza da imagem do heatmap e do app.py

.gitignore

@@ -16,3 +16,4 @@ env/
 .venv/
 pip-log.txt
 pip-delete-this-directory.txt
+.codex

app.py

@@ -14,11 +14,10 @@ from main import demo
 
 if __name__ == "__main__":
     port = int(os.environ.get("PORT", 7860))
-
-    logging.info(f"Starting app on port {port}")
+    logging.info("Starting app on port %s", port)
 
     demo.launch(
         server_name="0.0.0.0",
         server_port=port,
-        share=True
+        share=False,
     )

dockerfile

@@ -3,12 +3,16 @@ FROM python:3.10-slim
 ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
     PIP_NO_CACHE_DIR=1 \
-    HF_HOME=/data/.huggingface \
-    PORT=7860
+    PORT=7860 \
+    HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH \
+    HF_HOME=/tmp/.huggingface \
+    TRANSFORMERS_CACHE=/tmp/.cache/huggingface/transformers \
+    HUGGINGFACE_HUB_CACHE=/tmp/.cache/huggingface/hub
 
 RUN useradd -m -u 1000 user
 
-WORKDIR /app
+WORKDIR $HOME/app
 
 RUN apt-get update && apt-get install -y --no-install-recommends \
     build-essential \
@@ -22,15 +26,16 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     libgomp1 \
     && rm -rf /var/lib/apt/lists/*
 
-COPY --chown=user requirements.txt /app/requirements.txt
-RUN pip install --upgrade pip setuptools wheel && \
-    pip install -r /app/requirements.txt
+RUN mkdir -p /tmp/.huggingface /tmp/.cache/huggingface/transformers /tmp/.cache/huggingface/hub
 
-COPY --chown=user . /app
+COPY --chown=user requirements.txt $HOME/app/requirements.txt
 
 USER user
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
+
+RUN pip install --no-cache-dir --upgrade pip setuptools wheel && \
+    pip install --no-cache-dir -r $HOME/app/requirements.txt
+
+COPY --chown=user . $HOME/app
 
 EXPOSE 7860
 

requirements.txt

@@ -1,3 +1,5 @@
+fastapi
+uvicorn[standard]
 torch==2.4.1
 torchvision==0.19.1
 optuna==4.2.1

src/models/inference.py

@@ -315,9 +315,24 @@ def run_inference(image_pil, metadata_text, model_key=None):
         pred_class
     )
 
+    image_np = np.array(image_pil.convert("RGB"))
+    heatmap = np.asarray(heatmap, dtype=np.float32).squeeze()
+
+    if heatmap.shape != image_np.shape[:2]:
+        heatmap_tensor = torch.from_numpy(heatmap).unsqueeze(0).unsqueeze(0)
+        heatmap = torch.nn.functional.interpolate(
+            heatmap_tensor,
+            size=image_np.shape[:2],
+            mode="bilinear",
+            align_corners=False,
+        ).squeeze().cpu().numpy()
+
+    heatmap = np.clip(heatmap, 0.0, 1.0)
+    alpha_map = np.clip(heatmap * 0.6, 0.0, 0.6)
+
     fig, ax = plt.subplots(figsize=(6, 6))
-    ax.imshow(image_pil)
-    ax.imshow(heatmap, cmap="jet", alpha=0.4)
+    ax.imshow(image_np)
+    ax.imshow(heatmap, cmap="jet", alpha=alpha_map)
     ax.axis("off")
 
     title = f"{CLASS_LIST[pred_class]} | conf={confidence:.3f}"
@@ -328,4 +343,4 @@ def run_inference(image_pil, metadata_text, model_key=None):
     plt.close(fig)
 
     print(f"[inference] inference complete: {title}")
-    return result, title
+    return result, title