Update app.py

app.py

@@ -48,32 +48,41 @@ def get_deepseek_solution(disease_name):
 
 def response2(image, image_size=640, conf_threshold=0.3, iou_threshold=0.6):
     results = model.predict(image, conf=conf_threshold, iou=iou_threshold, imgsz=image_size)
-    
+
     text = ""
     solution = ""
-
     detected_diseases = set()
 
     for r in results:
         im_array = r.plot()
         im = Image.fromarray(im_array[..., ::-1]) 
+
     for r in results:
         conf = np.array(r.boxes.conf.cpu())
         cls = np.array(r.boxes.cls.cpu()).astype(int)
         xywh = np.array(r.boxes.xywh.cpu()).astype(int)
 
         for con, cl, xy in zip(conf, cls, xywh):
+            if cl < len(name):
+                disease_name = name[cl]
+            else:
+                disease_name = "Unknown"
+
             confidence = round(float(con) * 100, 1)
-            text += f"Detected {name[cl]} with confidence {confidence}% at ({xy[0]},{xy[1]})\n"
-            detected_diseases.add(name[cl])
+            text += f"Detected {disease_name} with confidence {confidence}% at ({xy[0]},{xy[1]})\n"
+            detected_diseases.add(disease_name)
+
+    explanation_cache = {}
 
     for disease in detected_diseases:
-        if disease != "Healthy":
-            solution += f"\n--- {disease} ---\n"
-            deepseek_explanation = get_deepseek_solution(disease)
-            solution += deepseek_explanation + "\n"
+        if disease.lower() == "healthy":
+            solution += f"\n--- {disease} ---\nTanaman tampak sehat. Tidak ada tindakan diperlukan.\n"
+        elif disease in name:
+            if disease not in explanation_cache:
+                explanation_cache[disease] = get_deepseek_solution(disease)
+            solution += f"\n--- {disease} ---\n{explanation_cache[disease]}\n"
         else:
-            solution += "\nTanaman tampak sehat. Tidak ada tindakan diperlukan.\n"
+            solution += f"\n--- {disease} ---\nJenis penyakit tidak dikenali. Tidak dapat memberikan solusi.\n"
 
     return im, text.strip(), solution.strip()
 
@@ -86,11 +95,11 @@ def process_video(video_path):
         ret, frame = cap.read()
         if not ret:
             break
-        pil_img = Image.fromarray(frame[..., ::-1])  
+        pil_img = Image.fromarray(frame[..., ::-1])
         result = model.predict(source=pil_img)
         for r in result:
             im_array = r.plot()
-            processed_frame = Image.fromarray(im_array[..., ::-1])  
+            processed_frame = Image.fromarray(im_array[..., ::-1])
         yield processed_frame
     cap.release()
 
@@ -103,7 +112,7 @@ inputs = [
 
 outputs = [
     gr.Image(type="pil", label="Output Image"),
-    gr.Textbox(label="Result"), 
+    gr.Textbox(label="Result"),
     gr.Textbox(label="AI-Powered Solution")
 ]
 
@@ -114,22 +123,22 @@ examples = [
 ]
 
 title = """Corn Diseases Detection Finetuned YOLOv11 <br></br> <a href="https://colab.research.google.com/drive/1vnxtgPKOgfC8nyCL9hjrNFed75StsqGQ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab" style="display:inline-block;"> </a> """
-description = 'Image Size: Defines the image size for inference.\nConfidence Treshold: Sets the minimum confidence threshold for detections.\nIOU Treshold: Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Useful for reducing duplicates.'
+description = 'Image Size: Ukuran gambar untuk inferensi.\nConfidence Threshold: Minimum confidence untuk deteksi.\nIOU Threshold: Threshold untuk Non-Maximum Suppression (NMS).'
 
 video_iface = gr.Interface(
     fn=process_video,
     inputs=gr.Video(label="Upload Video", interactive=True),
     outputs=gr.Image(type="pil", label="Result"),
     title=title,
-    description="Upload video for inference."
+    description="Upload video untuk deteksi penyakit jagung."
 )
 
 image_iface = gr.Interface(
-    fn=response2, 
-    inputs=inputs, 
-    outputs=outputs, 
-    examples=examples, 
-    title=title, 
+    fn=response2,
+    inputs=inputs,
+    outputs=outputs,
+    examples=examples,
+    title=title,
     description=description
 )
 
@@ -137,4 +146,3 @@ demo = gr.TabbedInterface([image_iface, video_iface], ["Image Inference", "Video
 
 if __name__ == '__main__':
     demo.launch()
-