File size: 6,989 Bytes
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import io
import pydicom
import numpy as np
from PIL import Image
import base64
import httpx
import asyncio
from dotenv import load_dotenv
load_dotenv()
HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
Z_AI_API_KEY = os.getenv("Z_AI_API_KEY")
ROBOFLOW_API_KEY = os.getenv("ROBOFLOW_API_KEY")
HEADERS_HF = {"Authorization": f"Bearer {HF_TOKEN}"}
HEADERS_Z_AI = {"Authorization": f"Bearer {Z_AI_API_KEY}", "Content-Type": "application/json"}
# List of models updated for 2026 SOTA
MODELS = {
"fracture": "bone-fracture-vqdiz/1", # Roboflow Object Detection (Local Docker)
"fracture_vlm": "AIRI-Institute/chexfract-maira2", # Medical VLM for Fracture Reports
"mammo": "ianpan/mammoscreen",
"breast_tissue": "nateraw/breast-cancer-classification",
"glm": "glm-4.6v"
}
async def call_hf_api(model_id, payload):
# Use standard Inference API endpoint for 2026 models
api_url = f"https://api-inference.huggingface.co/models/{model_id}"
async with httpx.AsyncClient(timeout=60.0) as client:
# Some models expect raw bytes, others expect json with 'inputs'
# For our 2026 ViT models, passing bytes directly often works better for image-classification
if "inputs" in payload and isinstance(payload["inputs"], str):
# If it's base64, we decode it back to bytes for the inference API raw call
try:
img_data = base64.b64decode(payload["inputs"])
response = await client.post(api_url, headers=HEADERS_HF, content=img_data)
except:
response = await client.post(api_url, headers=HEADERS_HF, json=payload)
else:
response = await client.post(api_url, headers=HEADERS_HF, json=payload)
if response.status_code == 503:
return {"error": "Model is loading", "details": response.json()}
return response.json()
async def call_roboflow_api(model_id, image_bytes):
"""
Appelle le serveur d'inférence Roboflow LOCAL (Docker).
Configuration basée sur la version RAG finale.
"""
# On utilise exactement les IDs de ton projet fonctionnel
API_KEY = os.getenv("ROBOFLOW_API_KEY", "Ac4Ngxa813phZbyPDo64")
PROJECT_ID = "bone-fracture-vqdiz"
MODEL_VERSION = "1"
# URL de ton Docker local
api_url = f"http://localhost:9001/{PROJECT_ID}/{MODEL_VERSION}?api_key={API_KEY}"
async with httpx.AsyncClient(timeout=30.0) as client:
# Envoi en multipart/form-data comme dans ton code med-ai-final
files = {'file': ("image.jpg", image_bytes, "image/jpeg")}
try:
response = await client.post(api_url, files=files)
print(f"DEBUG Roboflow Local: URL={api_url} Status={response.status_code}")
if response.status_code != 200:
print(f"DEBUG Roboflow Error Detail: {response.text}")
return {"error": response.text, "status": response.status_code}
return response.json()
except Exception as e:
print(f"DEBUG Roboflow Local Exception: {str(e)}")
return {
"error": "Serveur d'inférence local introuvable.",
"message": "Assurez-vous que le conteneur Docker tourne (inference server start).",
"details": str(e)
}
async def call_z_ai_api(payload):
api_url = "https://api.z.ai/api/paas/v4/chat/completions"
async with httpx.AsyncClient(timeout=90.0) as client:
response = await client.post(api_url, headers=HEADERS_Z_AI, json=payload)
return response.json()
def convert_dicom_to_jpg(dicom_bytes: bytes) -> bytes:
"""Convertit un fichier DICOM en image JPEG exploitable par l'IA."""
try:
ds = pydicom.dcmread(io.BytesIO(dicom_bytes))
pixel_array = ds.pixel_array
if hasattr(ds, 'RescaleIntercept') and hasattr(ds, 'RescaleSlope'):
pixel_array = pixel_array.astype(np.float32) * ds.RescaleSlope + ds.RescaleIntercept
p_min, p_max = np.min(pixel_array), np.max(pixel_array)
if p_max > p_min:
pixel_array = ((pixel_array - p_min) / (p_max - p_min) * 255).astype(np.uint8)
else:
pixel_array = pixel_array.astype(np.uint8)
img = Image.fromarray(pixel_array)
if img.mode != 'RGB':
img = img.convert('RGB')
buf = io.BytesIO()
img.save(buf, format="JPEG", quality=95)
return buf.getvalue()
except Exception as e:
print(f"Erreur conversion DICOM: {e}")
return None
async def run_radiology_agent(image_bytes: bytes, model_type: str, question: str = None) -> dict:
# Détection DICOM
is_dicom = False
if len(image_bytes) > 132 and image_bytes[128:132] == b"DICM":
is_dicom = True
if is_dicom:
converted = convert_dicom_to_jpg(image_bytes)
if converted:
image_bytes = converted
print("Image DICOM convertie avec succès.")
img_str = base64.b64encode(image_bytes).decode("utf-8")
if model_type == "glm":
payload = {
"model": "glm-4.6v",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": f"En tant qu'expert radiologue : {question if question else 'Décrivez cette imagerie et les anomalies potentielles.'}"
},
{
"type": "image_url",
"image_url": { "url": f"data:image/jpeg;base64,{img_str}" }
}
]
}
]
}
result = await call_z_ai_api(payload)
if "choices" in result:
return result["choices"][0]["message"]["content"]
return result
elif model_type == "fracture_vlm":
# Specific handling for 2026 ChexFract specialized VLM
model_id = MODELS.get("fracture_vlm")
# For VLMs on HF, they often expect a specific format or we use the chat template via inference
payload = {
"inputs": {
"image": img_str,
"text": question if question else "Describe any fractures in this X-ray and propose a BIRADS/Classification."
}
}
return await call_hf_api(model_id, payload)
else:
# Standard classification or specialized detection
model_id = MODELS.get(model_type)
if model_type == "fracture":
# Roboflow expects binary or base64 but our helper uses binary
return await call_roboflow_api(model_id, image_bytes)
payload = {
"inputs": img_str
}
return await call_hf_api(model_id, payload)
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