Update app.py

app.py

@@ -1,67 +1,68 @@
-from PIL import Image
-from transformers import pipeline
-import gradio as gr
-
-# 🌐 Load pre-trained image classification model
-classifier = pipeline("image-classification", model="microsoft/resnet-50")
-
-# 🔍 Define bilingual label mapping
-label_map = {
-    "agaric": ("Edible", "กินได้"),
-    "bolete": ("Edible", "กินได้"),
-    "gyromitra": ("Poisonous", "พิษ"),
-    "amanita": ("Poisonous", "พิษ"),
-    "earthstar": ("Edible", "กินได้"),
-    "hen-of-the-woods": ("Edible", "กินได้"),
-    "mushroom": ("Unknown", "ไม่ทราบ"),
-    "coral fungus": ("Poisonous", "พิษ"),
-    # Add more if needed
-}
-
-# 🧠 Classification function
-def classify_mushroom(image: Image.Image):
-    print("✅ classify_mushroom: NEW VERSION")
-
-    try:
-        image = image.convert("RGB")
-        result = classifier(image)
-        print("🔍 Raw result from model:", result)
-
-        result = result[0]
-        label = result['label'].lower()
-        score = round(result['score'] * 100, 2)
-
-        prediction_en, prediction_th = label_map.get(label, ("Unknown", "ไม่ทราบ"))
-        print("✅ RETURNING:", prediction_en, prediction_th, f"{score:.2f}%", label)
-
-        return prediction_en, prediction_th, f"{score:.2f}%", label
-
-    except Exception as e:
-        print(f"❌ Error: {e}")
-        return "Error", "ผิดพลาด", "N/A", "N/A"
-
-# 🎨 Gradio UI
-if __name__ == "__main__":
-    with gr.Blocks() as demo:
-        gr.Markdown("## 🍄 Mushroom Safety Classifier")
-        gr.Markdown("Upload a mushroom photo to check if it’s edible or poisonous.\nอัปโหลดรูปเห็ดเพื่อทำนายว่าเห็ดกินได้หรือมีพิษ")
-
-        with gr.Row():
-            image_input = gr.Image(type="pil", label="📷 Upload Mushroom Image")
-            with gr.Column():
-                label_en = gr.Textbox(label="🧠 Prediction (English)")
-                label_th = gr.Textbox(label="🗣️ คำทำนาย (ภาษาไทย)")
-                confidence = gr.Textbox(label="📶 Confidence Score")
-                label_raw = gr.Textbox(label="🏷️ Predicted Mushroom Name")
-
-        classify_btn = gr.Button("🔍 Classify")
-
-        classify_btn.click(
-            fn=classify_mushroom,
-            inputs=image_input,
-            outputs=[label_en, label_th, confidence, label_raw]
-        )
-
-    demo.launch()
-
-
+from PIL import Image
+from transformers import pipeline
+import gradio as gr
+
+# 🌐 Load pre-trained image classification model
+classifier = pipeline("image-classification", model="microsoft/resnet-50")
+
+# 🔍 Define bilingual label mapping
+label_map = {
+    "agaric": ("Edible", "กินได้"),
+    "bolete": ("Edible", "กินได้"),
+    "gyromitra": ("Poisonous", "พิษ"),
+    "amanita": ("Poisonous", "พิษ"),
+    "earthstar": ("Edible", "กินได้"),
+    "hen-of-the-woods": ("Edible", "กินได้"),
+    "mushroom": ("Unknown", "ไม่ทราบ"),
+    "coral fungus": ("Poisonous", "พิษ"),
+    "Amanita muscaria":("Poisonous","พิษ")
+    # Add more if needed
+}
+
+# 🧠 Classification function
+def classify_mushroom(image: Image.Image):
+    print("✅ classify_mushroom: NEW VERSION")
+
+    try:
+        image = image.convert("RGB")
+        result = classifier(image)
+        print("🔍 Raw result from model:", result)
+
+        result = result[0]
+        label = result['label'].lower()
+        score = round(result['score'] * 100, 2)
+
+        prediction_en, prediction_th = label_map.get(label, ("Unknown", "ไม่ทราบ"))
+        print("✅ RETURNING:", prediction_en, prediction_th, f"{score:.2f}%", label)
+
+        return prediction_en, prediction_th, f"{score:.2f}%", label
+
+    except Exception as e:
+        print(f"❌ Error: {e}")
+        return "Error", "ผิดพลาด", "N/A", "N/A"
+
+# 🎨 Gradio UI
+if __name__ == "__main__":
+    with gr.Blocks() as demo:
+        gr.Markdown("## 🍄 Mushroom Safety Classifier")
+        gr.Markdown("Upload a mushroom photo to check if it’s edible or poisonous.\nอัปโหลดรูปเห็ดเพื่อทำนายว่าเห็ดกินได้หรือมีพิษ")
+
+        with gr.Row():
+            image_input = gr.Image(type="pil", label="📷 Upload Mushroom Image")
+            with gr.Column():
+                label_en = gr.Textbox(label="🧠 Prediction (English)")
+                label_th = gr.Textbox(label="🗣️ คำทำนาย (ภาษาไทย)")
+                confidence = gr.Textbox(label="📶 Confidence Score")
+                label_raw = gr.Textbox(label="🏷️ Predicted Mushroom Name")
+
+        classify_btn = gr.Button("🔍 Classify")
+
+        classify_btn.click(
+            fn=classify_mushroom,
+            inputs=image_input,
+            outputs=[label_en, label_th, confidence, label_raw]
+        )
+
+    demo.launch()
+
+