Prepare Hugging Face Space

.gitattributes

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+*.pt filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.env
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+.gradio/

README.md

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+---
+title: Neuro-Oncology MRI Inference Console
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+python_version: "3.10"
+app_file: app.py
+suggested_hardware: cpu-basic
+---
+
+# Neuro-Oncology MRI Inference Console
+
+Gradio app for YOLO-based MRI lesion localization with structured explanation generated through the Groq chat-completions API.
+
+## Required Space secret
+
+- `GROQ_API_KEY`
+
+## Optional Space variable
+
+- `GROQ_MODEL`

app.py

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+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)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8bd1d5e3077a576bc6ec7e54e8d4d1949b78cede0340902a1b0155a66adf9f35
+size 207411165

requirements.txt

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+gradio==6.13.0
+ultralytics==8.4.41
+opencv-python-headless==4.13.0.92
+pillow==10.4.0
+requests==2.32.5
+torch==2.5.1
+python-dotenv==1.0.1