Minor Updates to app file

app.py

@@ -120,7 +120,7 @@ def identify_spots_or_lesions(img):
     spot_pixels = sum(cv2.contourArea(cnt) for cnt in filtered_contours)
     total_pixels = edges.shape[0] * edges.shape[1]
     percentage_spots = (spot_pixels / total_pixels)*100
-    print(f"Percentage of spots/lesions: {percentage_spots:.2f}%")
+    st.write(f"Percentage of spots/lesions: {percentage_spots:.2f}%")
 
     # Draw filtered contours
     contoured_image = cv2.drawContours(cv_image.copy(), filtered_contours, -1, (0, 255, 0), 1)
@@ -254,17 +254,17 @@ def classify_image(image):
     # --------------------------------
 
     if top_class_prob[0] < 0.999:  # threshold close to 1 to handle floating-point precision issues
-        print("Second predicted class:", second_class_name)
-        print(f"Second class confidence: {second_class_prob[0]:.3%}")
+        st.write("Second predicted class:", second_class_name)
+        st.write(f"Second class confidence: {second_class_prob[0]:.3%}")
     else:
-        print("Second predicted class: None")
+        st.write("Second predicted class: None")
 
     if "healthy" in predicted_class_name:
-        print(f"{predicted_class_name} is healthy, skipping further analysis.")
+        st.write(f"{predicted_class_name} is healthy, skipping further analysis.")
         return
     else:
         if "Background_without_leaves" in predicted_class_name:
-            print(f"{predicted_class_name} is not recognized as a plant image, skipping further analysis.")
+            st.write(f"{predicted_class_name} is not recognized as a plant image, skipping further analysis.")
             return
         else:
             spots_percentage = identify_spots_or_lesions(image)
@@ -276,7 +276,7 @@ def classify_image(image):
 
             if predicted_class_name in Details['Plant'].values:
                 row = Details.loc[Details['Plant'] == predicted_class_name].iloc[0]
-                st.write("Disease Identification:", row[4])
+                #st.write("Disease Identification:", row[4])
                 st.write("----------------------------------")
                 #st.write("Management:", row[5])
                 #st.markdown(severity_disease)
@@ -343,8 +343,8 @@ with tab2:
 
         result = {key: value.numpy() for key, value in result.items()}
 
-        print("Found %d objects." % len(result["detection_scores"]))
-        print("Inference time: ", end_time - start_time)
+        #st.write("Found %d objects." % len(result["detection_scores"]))
+        st.write("Inference time: ", end_time - start_time)
 
         detection_scores = result["detection_scores"]
         detection_class_entities = result["detection_class_entities"]
@@ -359,7 +359,7 @@ with tab2:
             entity = detection_class_entities[idx].decode('utf-8')
             if "Plant" == entity:
                 plant_score = detection_scores[idx]
-                print(f"Plant probability score: {plant_score:.2%}")
+                st.write(f"Plant probability score: {plant_score:.2%}")
                 result1 = classify_image(image)
 
                 if result1 is not None: