Update app.py

app.py

@@ -1,59 +1,56 @@
 """
-Glaucoma CDSS — HuggingFace Spaces
-Input : retinal fundus image (JPEG / PNG)
-Output: plain-text clinical report + downloadable PDF
+Glaucoma CDSS
+=============
+Gradio UI  : /
+REST API   : /predict  (JSON)  |  /predict/pdf  (PDF file)  |  /health
 """
 
-import os
-import sys
-import tempfile
+import os, sys, tempfile, shutil
+from pathlib import Path
 
 import cv2
 import numpy as np
 import gradio as gr
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse, FileResponse
 
 sys.path.insert(0, os.path.dirname(__file__))
-
 from phase3pipeline import Phase3Pipeline
 
-
-# ── CONFIG ────────────────────────────────────────────────────────────────
+# ── config ────────────────────────────────────────────────────────────────────
 REPO_ID = "Nj-1111/EyeeSEE"
-EPOCH = None
-TOKEN = os.getenv("HF_TOKEN_2") or os.getenv("HF_TOKEN")
-# ──────────────────────────────────────────────────────────────────────────
-
-print(f"Loading model repo={REPO_ID} epoch={'latest' if EPOCH is None else EPOCH}")
+EPOCH   = None
+TOKEN   = os.getenv("HF_TOKEN_2") or os.getenv("HF_TOKEN")
+# ─────────────────────────────────────────────────────────────────────────────
 
+print(f"Loading model  repo={REPO_ID}  epoch={'latest' if EPOCH is None else EPOCH}")
 pipeline = Phase3Pipeline(
-    repo_id=REPO_ID,
-    epoch=EPOCH,
-    mc_passes=1,
-    uncertainty_threshold=0.05,
-    token=TOKEN,
+    repo_id=REPO_ID, epoch=EPOCH, mc_passes=1,
+    uncertainty_threshold=0.05, token=TOKEN,
 )
-
 print("Model ready.")
 
 
-# ── IMAGE LOADER ──────────────────────────────────────────────────────────
+# ── shared helpers ────────────────────────────────────────────────────────────
+
 def _load_image(path: str) -> np.ndarray:
     img = cv2.imread(path)
-
     if img is None:
         raw = np.fromfile(path, dtype=np.uint8)
         img = cv2.imdecode(raw, cv2.IMREAD_COLOR)
-
     if img is None:
-        raise ValueError(f"Cannot read image: {path}")
-
+        raise ValueError(f"Cannot decode image: {path}")
     return img
 
 
-# ── REPORT BUILDER ────────────────────────────────────────────────────────
-def _build_text(r: dict, mc_passes: int) -> str:
-    isnt = r["isnt"]
+def _run_pipeline(path: str) -> dict:
+    img    = _load_image(path)
+    result = pipeline.run(img)
+    return result["report"]
 
+
+def _build_text(r: dict) -> str:
+    isnt = r["isnt"]
     lines = [
         "=" * 52,
         "  GLAUCOMA CDSS — CLINICAL REPORT",
@@ -74,93 +71,143 @@ def _build_text(r: dict, mc_passes: int) -> str:
         f"    Disc Area : {r['disc_area_px']:,} px",
         f"    Cup Area  : {r['cup_area_px']:,} px",
         f"    Cup/Disc  : {r['cup_area_px'] / max(r['disc_area_px'], 1) * 100:.1f}%",
-        f"    MC Passes : {mc_passes}",
+        f"    MC Passes : {pipeline.mc_passes}",
     ]
-    
-
-    warnings = r.get("warnings", [])
-    if warnings:
+    if r.get("warnings"):
         lines += ["", "  Warnings"]
-        for w in warnings:
+        for w in r["warnings"]:
             lines.append(f"    ! {w}")
-
     lines += [
-        "",
-        "=" * 52,
-        "  DISCLAIMER: Research prototype.",
-        "  Not a certified medical device.",
+        "", "=" * 52,
+        "  DISCLAIMER: Research prototype. Not a certified medical device.",
         "  Validate with a qualified ophthalmologist.",
         "=" * 52,
     ]
-
     return "\n".join(lines)
 
 
-# ── PDF BUILDER ───────────────────────────────────────────────────────────
-def _build_pdf(text: str):
-
-    try:
-        from fpdf import FPDF
-
-        # Remove unsupported unicode characters
-        safe_text = (
-            text.replace("—", "-")
-                .replace("•", "-")
-                .replace("→", "->")
-        )
+def _build_pdf(text: str) -> str:
+    from fpdf import FPDF
+    safe = text.replace("—", "-").replace("•", "-").replace("→", "->")
+    pdf  = FPDF()
+    pdf.add_page()
+    pdf.set_auto_page_break(auto=True, margin=15)
+    pdf.set_font("Courier", size=10)
+    for line in safe.splitlines():
+        pdf.multi_cell(0, 6, txt=line)
+    out = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+    pdf.output(out.name)
+    return out.name
 
-        pdf = FPDF()
 
-        pdf.add_page()
+def _save_upload(upload: UploadFile) -> str:
+    suffix = Path(upload.filename).suffix or ".jpg"
+    tmp    = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
+    shutil.copyfileobj(upload.file, tmp)
+    tmp.flush()
+    return tmp.name
 
-        pdf.set_auto_page_break(auto=True, margin=15)
 
-        pdf.set_font("Courier", size=10)
+# ── FastAPI app + endpoints ───────────────────────────────────────────────────
 
-        for line in safe_text.splitlines():
-            pdf.multi_cell(0, 6, txt=line)
+app = FastAPI(title="Glaucoma CDSS API", version="1.0")
 
-        out = tempfile.NamedTemporaryFile(
-            delete=False,
-            suffix=".pdf"
-        )
 
-        pdf.output(out.name)
+@app.get("/health")
+def health():
+    """Check that the model is loaded and ready."""
+    return {
+        "status": "ok",
+        "repo":   REPO_ID,
+        "epoch":  "latest" if EPOCH is None else EPOCH,
+    }
 
-        return out.name
 
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    """
+    Run glaucoma screening on an uploaded fundus image.
+
+    Request  : multipart/form-data  field name = file
+    Accepts  : JPEG, PNG
+    Response : JSON clinical report
+
+    JSON fields
+    -----------
+    vcdr              float   vertical cup-to-disc ratio
+    risk_level        string  Healthy | Glaucoma Suspect | High Risk
+    uncertainty       float   MC-Dropout variance (lower = more confident)
+    sanity_passed     bool    True if cup is naturally inside disc
+    isnt              object  inferior/superior/nasal/temporal rim thickness
+    structural        object  disc_area_px, cup_area_px, cup_disc_pct
+    warnings          list    clinical warning strings
+    """
+    path = _save_upload(file)
+    try:
+        r = _run_pipeline(path)
     except Exception as e:
+        raise HTTPException(status_code=422, detail=str(e))
+    finally:
+        os.remove(path)
+
+    return JSONResponse({
+        "vcdr":          r["vcdr"],
+        "risk_level":    r["risk_level"],
+        "uncertainty":   r["uncertainty"],
+        "sanity_passed": r["sanity_passed"],
+        "isnt": {
+            "inferior":       r["isnt"]["inferior"],
+            "superior":       r["isnt"]["superior"],
+            "nasal":          r["isnt"]["nasal"],
+            "temporal":       r["isnt"]["temporal"],
+            "rule_satisfied": r["isnt"]["rule_satisfied"],
+        },
+        "structural": {
+            "disc_area_px":  r["disc_area_px"],
+            "cup_area_px":   r["cup_area_px"],
+            "cup_disc_pct":  round(r["cup_area_px"] / max(r["disc_area_px"], 1) * 100, 1),
+        },
+        "warnings": r.get("warnings", []),
+    })
+
+
+@app.post("/predict/pdf")
+async def predict_pdf(file: UploadFile = File(...)):
+    """
+    Run glaucoma screening and return a downloadable PDF report.
 
-        print("PDF generation failed:", repr(e))
+    Request  : multipart/form-data  field name = file
+    Accepts  : JPEG, PNG
+    Response : application/pdf
+    """
+    path = _save_upload(file)
+    try:
+        r    = _run_pipeline(path)
+        text = _build_text(r)
+        pdf  = _build_pdf(text)
+    except Exception as e:
+        raise HTTPException(status_code=422, detail=str(e))
+    finally:
+        os.remove(path)
+
+    return FileResponse(
+        pdf,
+        media_type="application/pdf",
+        filename="glaucoma_report.pdf",
+    )
 
-        return None
 
+# ── Gradio UI (mounted at /) ──────────────────────────────────────────────────
 
-# ── INFERENCE ─────────────────────────────────────────────────────────────
 def analyse(file_path):
     if file_path is None:
         return "No file uploaded.", None, "Please upload a JPEG or PNG fundus image."
-
     try:
-        print(f"Received file: {file_path}")
-
-        img = _load_image(file_path)
-        print(f"Image loaded successfully. Shape: {img.shape}")
-
-        result = pipeline.run(img)
-        print("Pipeline inference completed.")
-
-        text = _build_text(result["report"], pipeline.mc_passes)
-        pdf = _build_pdf(text)
-
-        status = "Analysis completed successfully."
-        if pdf is None:
-            status = "Analysis completed, but PDF generation failed."
-
-        return text, pdf, status
-
+        r    = _run_pipeline(file_path)
+        text = _build_text(r)
+        pdf  = _build_pdf(text)
+        return text, pdf, "Analysis completed."
     except Exception as e:
-        print(f"Analysis error: {e}")
         return f"Error: {e}", None, f"Analysis failed: {e}"
 
 
@@ -172,12 +219,9 @@ def set_ready():
     return gr.update(interactive=True)
 
 
-# ── UI ────────────────────────────────────────────────────────────────────
-with gr.Blocks(title="Glaucoma CDSS") as demo:
-    gr.Markdown(
-        "## Glaucoma CDSS\n"
-        "Upload a retinal fundus image to receive a clinical screening report."
-    )
+with gr.Blocks(title="Glaucoma CDSS") as gradio_ui:
+
+    gr.Markdown("## Glaucoma CDSS\nUpload a retinal fundus image to receive a clinical screening report.")
 
     with gr.Row():
         file_in = gr.File(
@@ -185,131 +229,43 @@ with gr.Blocks(title="Glaucoma CDSS") as demo:
             file_types=[".jpg", ".jpeg", ".png"],
             type="filepath",
         )
-
         run_btn = gr.Button("Analyse", variant="primary")
 
-    status_box = gr.Textbox(
-        label="Status",
-        value="Awaiting image upload.",
-        interactive=False,
-    )
-
-    report_box = gr.Textbox(
-        label="Clinical Report",
-        lines=28,
-        interactive=False,
-    )
-
-    pdf_out = gr.File(label="Download PDF Report")
-
-    gr.Markdown(
-        "_Research prototype — NOT a medical device. "
-        "All results must be reviewed by a qualified ophthalmologist._"
-    )
-    gr.Markdown(
-    """
-# Glaucoma CDSS
-
-Upload a retinal fundus image to generate an AI-assisted glaucoma screening report.
-
----
-
-## How to Interpret the Report
-
-### 1. vCDR (Vertical Cup-to-Disc Ratio)
-The most important glaucoma screening metric.
-General interpretation:
-- **0.30 – 0.50** → Usually within healthy range
-- **0.50 – 0.65** → Borderline / monitor carefully
-- **0.65 – 0.80** → Glaucoma suspect
-- **> 0.80** → High glaucoma risk
-
-Higher values indicate enlargement of the optic cup relative to the optic disc.
-
----
-
-## 2. ISNT Rule
-
-Healthy optic nerves typically follow:
-**Inferior > Superior > Nasal > Temporal**
-This is called the **ISNT Rule**.
-
-- **Satisfied** → Anatomically more normal
-- **Violated** → Possible neuro-retinal rim thinning associated with glaucoma
-
-ISNT violation alone does NOT confirm glaucoma, but it is an important warning sign.
-
----
-
-## 3. Uncertainty Score
-Represents model confidence.
-- **< 0.05** → Stable prediction
-- **0.05 – 0.10** → Moderate uncertainty
-- **> 0.10** → Low confidence prediction
-
-High uncertainty may occur with:
-- poor image quality,
-- blur,
-- extreme lighting,
-- incomplete optic disc visibility.
-
----
-
-## 4. Structural Measurements
-
-### Disc Area
-Estimated optic disc size in pixels.
-### Cup Area
-Estimated optic cup size in pixels.
-### Cup/Disc %
-Percentage of cup area relative to disc area.
-Larger cup proportions may indicate glaucomatous damage.
+    status_box  = gr.Textbox(label="Status", value="Awaiting upload.", interactive=False)
+    report_box  = gr.Textbox(label="Clinical Report", lines=28, interactive=False)
+    pdf_out     = gr.File(label="Download PDF Report")
 
+    gr.Markdown("""
 ---
+### How to interpret results
 
-## 5. Risk Levels
+| vCDR | Indication |
+|------|------------|
+| 0.30 – 0.50 | Usually healthy |
+| 0.50 – 0.65 | Borderline |
+| 0.65 – 0.80 | Glaucoma suspect |
+| > 0.80 | High risk |
 
-### Healthy
-No major structural glaucoma indicators detected.
-### Glaucoma Suspect
-One or more warning signs detected:
-- elevated vCDR,
-- ISNT violation,
-- anatomical inconsistency,
-- or uncertain segmentation.
+**ISNT Rule** — healthy nerves follow Inferior > Superior > Nasal > Temporal.
+Violation suggests neuro-retinal rim thinning.
 
-### High Risk
-Strong structural indicators of glaucoma detected.
-Clinical ophthalmology review is strongly recommended.
+**Uncertainty** — values above 0.05 indicate low model confidence (check image quality).
 
 ---
-
-## Important Disclaimer
-This system is a **research prototype** and NOT a certified medical device.
-The generated report is intended for:
-- educational use,
-- AI research,
-- and preliminary screening assistance only.
-
-All clinical decisions must be made by a qualified ophthalmologist.
-"""
-)
+*Research prototype — NOT a medical device. All results must be reviewed by a qualified ophthalmologist.*
+""")
 
     run_btn.click(
-        fn=set_busy,
-        inputs=None,
-        outputs=[run_btn, status_box],
-        queue=False,
+        fn=set_busy, inputs=None, outputs=[run_btn, status_box], queue=False,
     ).then(
-        fn=analyse,
-        inputs=[file_in],
-        outputs=[report_box, pdf_out, status_box],
+        fn=analyse, inputs=[file_in], outputs=[report_box, pdf_out, status_box],
     ).then(
-        fn=set_ready,
-        inputs=None,
-        outputs=[run_btn],
-        queue=False,
+        fn=set_ready, inputs=None, outputs=[run_btn], queue=False,
     )
 
+
+app = gr.mount_gradio_app(app, gradio_ui, path="/")
+
 if __name__ == "__main__":
-    demo.launch()
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)