| import os |
|
|
| import gradio as gr |
| import requests |
| from dotenv import load_dotenv |
| from PIL import ImageDraw, ImageFont |
| from ultralytics import YOLO |
|
|
| YOLO_WEIGHTS = "best.pt" |
| GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions" |
|
|
| WINDOWS_XP_COLORS = { |
| "bg": "#ece9d8", |
| "title": "#0053e1", |
| "status": "#f3f3f3", |
| "border": "#808080", |
| } |
|
|
| load_dotenv() |
| GROQ_API_KEY = os.getenv("GROQ_API_KEY", "") |
| GROQ_MODEL = os.getenv("GROQ_MODEL", "llama-3.3-70b-versatile") |
|
|
| custom_css = f""" |
| body {{ background: {WINDOWS_XP_COLORS["bg"]}; font-family: Tahoma, Verdana, sans-serif; }} |
| .gradio-container {{ |
| border: 2px solid {WINDOWS_XP_COLORS["border"]}; |
| background: {WINDOWS_XP_COLORS["bg"]}; |
| border-radius: 6px; |
| max-width: 700px; |
| margin: 32px auto; |
| box-shadow: 0 4px 16px #bbb; |
| }} |
| .gradio-title {{ |
| background: {WINDOWS_XP_COLORS["title"]}; |
| color: #fff; |
| padding: 10px 16px; |
| font-size: 20px; |
| border-top-left-radius: 6px; |
| border-top-right-radius: 6px; |
| margin-bottom: 0; |
| }} |
| .status-bar {{ |
| background: {WINDOWS_XP_COLORS["status"]}; |
| color: #333; |
| padding: 6px 16px; |
| font-size: 13px; |
| border-bottom-left-radius: 6px; |
| border-bottom-right-radius: 6px; |
| border-top: 1px solid {WINDOWS_XP_COLORS["border"]}; |
| margin-top: 0; |
| }} |
| """ |
|
|
|
|
| class DetectionModule: |
| def __init__(self, weights_path): |
| if not os.path.exists(weights_path): |
| raise FileNotFoundError(f"YOLO weights not found: {weights_path}") |
| self.model = YOLO(weights_path) |
|
|
| def run(self, image): |
| if image is None: |
| return [] |
| results = self.model(image, verbose=False) |
| detections = [] |
| for result in results: |
| names = result.names |
| for box in result.boxes: |
| cls_idx = int(box.cls.item()) |
| conf = float(box.conf.item()) |
| x1, y1, x2, y2 = box.xyxy[0].tolist() |
| detections.append( |
| { |
| "class": names.get(cls_idx, str(cls_idx)), |
| "conf": conf, |
| "box": [x1, y1, x2, y2], |
| } |
| ) |
| return detections |
|
|
|
|
| class ExplanationModule: |
| def __init__(self, api_key, api_url=GROQ_API_URL): |
| self.api_key = api_key |
| self.api_url = api_url |
|
|
| def generate(self, detections): |
| if not self.api_key: |
| return "[Groq API key not set. Cannot generate explanation.]" |
| if not detections: |
| return "No tumor detected with sufficient confidence." |
| det_lines = [f"- Tumor type: {d['class']}, Confidence: {d['conf']:.2f}" for d in detections] |
| prompt = ( |
| "You are a medical AI assistant.\n" |
| "Input:\n" |
| f"Detection count: {len(detections)}\n" |
| + "\n".join(det_lines) |
| + "\nExplain in simple terms:\n" |
| "- What was detected\n" |
| "- What confidence means\n" |
| "- Avoid medical diagnosis\n" |
| "- Add disclaimer\n" |
| ) |
| headers = { |
| "Authorization": f"Bearer {self.api_key}", |
| "Content-Type": "application/json", |
| } |
| data = { |
| "model": GROQ_MODEL, |
| "messages": [{"role": "user", "content": prompt}], |
| "max_tokens": 256, |
| "temperature": 0.2, |
| } |
| try: |
| response = requests.post(self.api_url, headers=headers, json=data, timeout=10) |
| response.raise_for_status() |
| payload = response.json() |
| return payload["choices"][0]["message"]["content"].strip() |
| except Exception as exc: |
| return f"[Groq API error: {exc}]" |
|
|
|
|
| class VisualizationPipeline: |
| def __init__(self): |
| self.font = ImageFont.load_default() |
| self.box_color = (0, 83, 225) |
| self.text_color = (0, 0, 0) |
|
|
| def draw(self, image, detections): |
| rendered = image.convert("RGB").copy() |
| draw = ImageDraw.Draw(rendered) |
| for detection in detections: |
| x1, y1, x2, y2 = map(int, detection["box"]) |
| label = f"{detection['class']} ({detection['conf']:.2f})" |
| draw.rectangle([x1, y1, x2, y2], outline=self.box_color, width=3) |
| draw.text((x1, max(0, y1 - 16)), label, fill=self.text_color, font=self.font) |
| return rendered |
|
|
|
|
| class InferenceOrchestrator: |
| def __init__(self, detection_module, explanation_module, visualization): |
| self.detection = detection_module |
| self.explanation = explanation_module |
| self.visualization = visualization |
|
|
| def predict(self, image): |
| detections = self.detection.run(image) |
| visual = self.visualization.draw(image, detections) |
| explanation = self.explanation.generate(detections) |
| if detections: |
| top = max(detections, key=lambda item: item["conf"]) |
| return visual, top["class"], top["conf"], explanation |
| return visual, "no tumor", 0.0, explanation |
|
|
|
|
| detection_module = DetectionModule(YOLO_WEIGHTS) |
| explanation_module = ExplanationModule(GROQ_API_KEY) |
| visualization = VisualizationPipeline() |
| orchestrator = InferenceOrchestrator(detection_module, explanation_module, visualization) |
|
|
|
|
| def set_ready(): |
| return "Ready" |
|
|
|
|
| def analyze(image): |
| if image is None: |
| return "Upload an MRI image to analyze.", None, "", 0.0, "" |
| visual, tumor, conf, expl = orchestrator.predict(image) |
| return "Analysis complete.", visual, tumor, conf, expl |
|
|
|
|
| with gr.Blocks(title="Neuro-Oncology MRI Inference Console") as demo: |
| gr.Markdown( |
| "<div class='gradio-title'>Neuro-Oncology MRI Inference Console</div>" |
| "<div class='status-bar'>YOLO-based lesion localization with structured LLM-assisted explanation for research workflows.</div>" |
| ) |
| with gr.Row(): |
| with gr.Column(): |
| image_in = gr.Image(type="pil", label="Upload MRI Image", elem_id="img-in") |
| status = gr.Markdown("Initializing inference pipeline...", elem_id="status-bar") |
| with gr.Column(): |
| image_out = gr.Image(type="pil", label="Annotated MRI Output", elem_id="img-out") |
| tumor_type = gr.Textbox(label="Predicted Finding", interactive=False) |
| confidence = gr.Number(label="Detection Confidence", interactive=False) |
| explanation = gr.Textbox(label="Structured Interpretation Summary", lines=6, interactive=False) |
| demo.load(set_ready, None, status) |
| analyze_btn = gr.Button("Run Inference", elem_id="analyze-btn", interactive=True) |
| analyze_btn.click( |
| analyze, |
| inputs=[image_in], |
| outputs=[status, image_out, tumor_type, confidence, explanation], |
| ) |
| gr.Markdown("<div class='status-bar'>For research use only. Not for clinical diagnosis.</div>") |
|
|
|
|
| if __name__ == "__main__": |
| launch_kwargs = { |
| "theme": gr.themes.Base(), |
| "css": custom_css, |
| "show_error": True, |
| } |
| if os.getenv("SPACE_ID"): |
| launch_kwargs["server_name"] = "0.0.0.0" |
| port = os.getenv("PORT") |
| if port: |
| launch_kwargs["server_port"] = int(port) |
| demo.launch(**launch_kwargs) |
|
|
