Update app.py

app.py

@@ -1,133 +1,65 @@
-from flask import Flask, request, jsonify
-from gradio_client import Client, handle_file
-import tempfile, base64, os, threading
-from huggingface_hub import InferenceClient
-
-from flask_cors import CORS  # ✅ allow mobile apps to call this API
-
-app = Flask(__name__)
-CORS(app)  # ✅ enable CORS for all routes
-
-# Your Hugging Face Spaces
-GINGIVITIS_SPACE = "jayn95/deepdent_gingivitis"
-PERIODONTITIS_SPACE = "jayn95/deepdent_periodontitis"
-
-
-def call_huggingface(space_name, image_path, labels=None, flatten=False, timeout_seconds=120):
-    """Call HF Space in a separate thread with timeout."""
-    client = Client(space_name)
-    result_container = {}
-
-    def run_predict():
-        result_container["data"] = client.predict(
-            handle_file(image_path),
-            0.4,
-            0.5,
-            api_name="/predict"
-        )
-
-    thread = threading.Thread(target=run_predict)
-    thread.start()
-    thread.join(timeout=timeout_seconds)
-
-    if thread.is_alive():
-        raise TimeoutError(f"Hugging Face request to {space_name} timed out after {timeout_seconds}s")
-
-    result = result_container.get("data", [])
-
-    # Flatten nested list if needed
-    flat_result = []
-    if flatten:
-        for r in result:
-            if isinstance(r, (list, tuple)):
-                flat_result.extend(r)
-            else:
-                flat_result.append(r)
-    else:
-        flat_result = result
-
-    # Auto-generate labels if None
-    if labels is None:
-        labels = []
-        if space_name == PERIODONTITIS_SPACE:
-            num_teeth = len(flat_result) // 2
-            for i in range(num_teeth):
-                for m in ["cej", "abc"]:
-                    labels.append(f"tooth{i+1}_{m}")
+# app.py
+import gradio as gr
+import cv2
+from periodontitis_detection import SimpleDentalSegmentationNoEnhance
+
+# ==========================
+# 1️⃣ Load models once
+# ==========================
+model = SimpleDentalSegmentationNoEnhance(
+    unet_model_path="unet.keras",   # same filenames as your repo
+    yolo_model_path="best2.pt"
+)
+
+# ==========================
+# 2️⃣ Define wrapper for Gradio
+# ==========================
+def detect_periodontitis(image_np):
+    """
+    Gradio sends image as a NumPy RGB array.
+    We temporarily save it to a file path since analyze_image() needs a path.
+    """
+    temp_path = "temp_input.jpg"
+    cv2.imwrite(temp_path, cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR))
+
+    # Run full pipeline
+    results = model.analyze_image(temp_path)
+
+    # Convert OpenCV BGR → RGB for Gradio display
+    combined_rgb = cv2.cvtColor(results["combined"], cv2.COLOR_BGR2RGB)
+
+    # Optional: summarize measurements for text output
+    summaries = []
+    for tooth in results["distance_analyses"]:
+        tooth_id = tooth["tooth_id"]
+        analysis = tooth["analysis"]
+        if analysis:
+            mean_d = analysis["mean_distance"]
+            summaries.append(f"Tooth {tooth_id}: mean={mean_d:.2f}px")
         else:
-            labels = [f"output{i+1}" for i in range(len(flat_result))]
-
-    # Encode results as base64
-    encoded_results = {}
-    for label, path in zip(labels, flat_result):
-        if os.path.exists(path):
-            with open(path, "rb") as f:
-                encoded_results[label] = base64.b64encode(f.read()).decode("utf-8")
-        else:
-            encoded_results[label] = None
-
-    return encoded_results
-
-
-@app.route("/")
-def home():
-    return jsonify({"status": "DeepDent backend running successfully!"})
-
-
-@app.route("/predict/gingivitis", methods=["POST"])
-def predict_gingivitis():
-    try:
-        image = request.files.get("image")
-        if not image:
-            return jsonify({"error": "No image provided"}), 400
-
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
-            image.save(temp_file.name)
-            temp_path = temp_file.name
-
-        encoded_results = call_huggingface(
-            GINGIVITIS_SPACE,
-            temp_path,
-            labels=["swelling", "redness", "bleeding"]
-        )
-
-        os.remove(temp_path)
-        return jsonify({"images": encoded_results})
-
-    except TimeoutError as te:
-        return jsonify({"error": str(te)}), 504
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-
-@app.route("/predict/periodontitis", methods=["POST"])
-def predict_periodontitis():
-    try:
-        image = request.files.get("image")
-        if not image:
-            return jsonify({"error": "No image provided"}), 400
-
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
-            image.save(temp_file.name)
-            temp_path = temp_file.name
-
-        encoded_results = call_huggingface(
-            PERIODONTITIS_SPACE,
-            temp_path,
-            labels=None,
-            flatten=True
-        )
-
-        os.remove(temp_path)
-        return jsonify({"images": encoded_results})
-
-    except TimeoutError as te:
-        return jsonify({"error": str(te)}), 504
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-
-# 🚀 Required function for Hugging Face Spaces
-def start():
-    # HF Spaces automatically assigns host/port
-    app.run()
+            summaries.append(f"Tooth {tooth_id}: no valid CEJ–ABC measurement")
+
+    summary_text = "\n".join(summaries) if summaries else "No detections found."
+
+    return combined_rgb, summary_text
+
+
+# ==========================
+# 3️⃣ Build Gradio Interface
+# ==========================
+demo = gr.Interface(
+    fn=detect_periodontitis,
+    inputs=gr.Image(type="numpy", label="Upload Dental X-Ray"),
+    outputs=[
+        gr.Image(label="Final Annotated Image (YOLO + CEJ–ABC)"),
+        gr.Textbox(label="Analysis Summary"),
+    ],
+    title="🦷 Periodontitis Detection & Analysis",
+    description=(
+        "Automatically detects teeth (YOLOv8), segments CEJ/ABC (U-Net), "
+        "and measures CEJ–ABC distances per tooth to assess bone loss."
+    ),
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)