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Create app.py
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app.py
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from fastapi import FastAPI, HTTPException, Response
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from pydantic import BaseModel
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import io
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import base64
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import blosc
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import numpy as np
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import matplotlib.pyplot as plt
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# --- App and Pydantic Model Setup ---
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app = FastAPI()
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class BinaryPayload(BaseModel):
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file_data: str
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dtype: str = 'float32'
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shape: tuple[int, int] = (720, 1440)
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cmap: str = 'plasma'
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# --- Helper Function ---
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def normalize(arr: np.ndarray, lo: float, hi: float) -> np.ndarray:
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"""Normalizes a NumPy array to the 0-1 range for visualization."""
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return np.clip((arr - lo) / (hi - lo), 0, 1)
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# --- API Endpoints ---
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@app.get("/")
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def home():
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return {"message": "Binary to PNG Conversion API is live."}
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@app.post("/render-from-binary")
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async def render_from_binary(payload: BinaryPayload):
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"""
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Accepts a base64-encoded, Blosc-compressed binary chunk
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and returns it as a rendered PNG image.
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"""
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try:
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# Decode the base64 string
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compressed_data = base64.b64decode(payload.file_data)
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# Decompress the data using Blosc
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decompressed_data = blosc.decompress(compressed_data)
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# Interpret bytes as a NumPy array and reshape it
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image_array = np.frombuffer(decompressed_data, dtype=payload.dtype)
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image_array = image_array.reshape(payload.shape)
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# Normalize the array for proper color mapping
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vmin = float(np.nanmin(image_array))
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vmax = float(np.nanmax(image_array))
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normalized_frame = normalize(image_array, vmin, vmax)
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# Generate the PNG image using Matplotlib
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img_buf = io.BytesIO()
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fig, ax = plt.subplots(figsize=(6, 3), dpi=240)
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ax.imshow(normalized_frame, cmap=payload.cmap, origin="upper")
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ax.axis("off")
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plt.savefig(img_buf, format="png", bbox_inches="tight", pad_inches=0)
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plt.close(fig)
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img_buf.seek(0)
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# Return the image as a response
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return Response(content=img_buf.read(), media_type="image/png")
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except base64.binascii.Error as e:
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raise HTTPException(status_code=400, detail=f"Invalid base64 string: {e}")
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except Exception as e:
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raise HTTPException(status_code=400, detail=f"Failed to process binary data: {e}")
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