Update app.py

app.py

@@ -1,64 +1,64 @@
-import gradio as gr
-import os
-import uuid
-from PIL import Image
-import sys
-
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'src')))
-from predict import predict_image
-
-UPLOAD_DIR = os.path.join(os.path.dirname(__file__), 'uploads')
-os.makedirs(UPLOAD_DIR, exist_ok=True)
-
-def classify(image):
-    path = None
-    try:
-        filename = f"{uuid.uuid4()}.jpg"
-        path = os.path.join(UPLOAD_DIR, filename)
-        image.save(path)
-
-        label, confidence = predict_image(path)
-        return f"Tahmin: {label} - Güven: {confidence:.2f}"
-    except Exception as e:
-        print("❌ Hata:", str(e))
-        return f"HATA: {str(e)}"
-    finally:
-        if path and os.path.exists(path):
-            os.remove(path)
-
-with gr.Blocks(css="""
-    #main-box {
-        max-width: 600px;
-        margin: auto;
-    }
-
-    #submit-btn {
-        background-color: #c2410c !important; /* turuncu */
-        color: white !important;
-        border-radius: 6px;
-    }
-
-    #clear-btn {
-        background-color: #4b5563 !important; /* koyu gri */
-        color: white !important;
-        border-radius: 6px;
-    }
-""") as demo:
-    with gr.Column(elem_id="main-box"):
-        gr.Markdown("## 🧠 Beyin Tümörü Tespiti\nMRI görüntüsünü yükleyin, tahmin et butonuna basın.")
-
-        image_input = gr.Image(type="pil", label="MRI Görüntüsü Yükle")
-
-        with gr.Row():
-            submit_btn = gr.Button("Submit", elem_id="submit-btn")
-            clear_btn = gr.Button("Clear", elem_id="clear-btn")
-
-        result_output = gr.Textbox(label="Tahmin Sonucu")
-
-    submit_btn.click(fn=classify, inputs=image_input, outputs=result_output)
-    clear_btn.click(fn=lambda: (None, ""), outputs=[image_input, result_output])
-
-demo.launch(share=True)
-
-
-
+import gradio as gr
+import os
+import uuid
+from PIL import Image
+import sys
+
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'src')))
+from src.predict import predict_image
+
+UPLOAD_DIR = os.path.join(os.path.dirname(__file__), 'uploads')
+os.makedirs(UPLOAD_DIR, exist_ok=True)
+
+def classify(image):
+    path = None
+    try:
+        filename = f"{uuid.uuid4()}.jpg"
+        path = os.path.join(UPLOAD_DIR, filename)
+        image.save(path)
+
+        label, confidence = predict_image(path)
+        return f"Tahmin: {label} - Güven: {confidence:.2f}"
+    except Exception as e:
+        print("❌ Hata:", str(e))
+        return f"HATA: {str(e)}"
+    finally:
+        if path and os.path.exists(path):
+            os.remove(path)
+
+with gr.Blocks(css="""
+    #main-box {
+        max-width: 600px;
+        margin: auto;
+    }
+
+    #submit-btn {
+        background-color: #c2410c !important; /* turuncu */
+        color: white !important;
+        border-radius: 6px;
+    }
+
+    #clear-btn {
+        background-color: #4b5563 !important; /* koyu gri */
+        color: white !important;
+        border-radius: 6px;
+    }
+""") as demo:
+    with gr.Column(elem_id="main-box"):
+        gr.Markdown("## 🧠 Beyin Tümörü Tespiti\nMRI görüntüsünü yükleyin, tahmin et butonuna basın.")
+
+        image_input = gr.Image(type="pil", label="MRI Görüntüsü Yükle")
+
+        with gr.Row():
+            submit_btn = gr.Button("Submit", elem_id="submit-btn")
+            clear_btn = gr.Button("Clear", elem_id="clear-btn")
+
+        result_output = gr.Textbox(label="Tahmin Sonucu")
+
+    submit_btn.click(fn=classify, inputs=image_input, outputs=result_output)
+    clear_btn.click(fn=lambda: (None, ""), outputs=[image_input, result_output])
+
+demo.launch(share=True)
+
+
+