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Dockerfile

@@ -6,16 +6,18 @@ WORKDIR /app
 
 # 3. Install PyTorch (CPU version to save massive space/RAM) and required libraries
 RUN pip install --no-cache-dir torch torchvision --index-url https://download.pytorch.org/whl/cpu
-RUN pip install --no-cache-dir fastapi uvicorn transformers pillow pydantic
 
-# 4. THE CHEAT CODE: Pre-download the heavy model weights during the build phase
+# 4. Install API and networking dependencies (requests added here!)
+RUN pip install --no-cache-dir fastapi uvicorn transformers pillow pydantic requests
+
+# 5. THE CHEAT CODE: Pre-download the heavy model weights during the build phase
 RUN python -c "from transformers import pipeline; pipeline('image-classification', model='dima806/chest_xray_pneumonia_detection')"
 
-# 5. Copy your FastAPI code into the container
+# 6. Copy your FastAPI code into the container
 COPY app.py .
 
-# 6. Expose the exact port Hugging Face Spaces requires
+# 7. Expose the exact port Hugging Face Spaces requires
 EXPOSE 7860
 
-# 7. Start the node
+# 8. Start the node
 CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]