Update streamlit_app.py

streamlit_app.py

@@ -12,7 +12,7 @@ warnings.filterwarnings('ignore')
 
 # Configure Streamlit
 st.set_page_config(
-    page_title="Butterfly Identifier/Liblikamaja ID",
+    page_title="Butterfly Identifier/ Liblikamaja ID",
     page_icon="🦋",
     layout="wide"
 )
@@ -261,65 +261,51 @@ transform = transforms.Compose([
     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
 ])
 
-def predict_butterfly(image):
+def predict_butterfly(image, threshold=0.5):
     """Predict butterfly species from image"""
-    if image is None or model is None:
+    if image is None:
         return None, None
     
-    try:
-        # Convert to PIL Image if needed
-        if isinstance(image, np.ndarray):
-            image = Image.fromarray(image)
-        
-        # Ensure RGB format
-        if image.mode != 'RGB':
-            image = image.convert('RGB')
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+    
+    input_tensor = transform(image).unsqueeze(0)
+
+    with torch.no_grad():
+        output = model(input_tensor)
+        probabilities = torch.nn.functional.softmax(output[0], dim=0)
+
+        confidence, pred = torch.max(probabilities, 0)
+        if confidence.item() < threshold:
+            return None, confidence.item()  # Too uncertain
         
-        # Preprocess
-        input_tensor = transform(image).unsqueeze(0)
+        predicted_class = class_names[pred.item()]
         
-        # Predict
-        with torch.no_grad():
-            output = model(input_tensor)
-            probabilities = torch.nn.functional.softmax(output[0], dim=0)
-            confidence, pred = torch.max(probabilities, 0)
-            
-            # Get top 3 predictions for better debugging
-            top_probs, top_indices = torch.topk(probabilities, min(3, len(probabilities)))
-            
-            if pred.item() < len(class_names):
-                predicted_class = class_names[pred.item()]
-            else:
-                predicted_class = f"Class_{pred.item()}"
-            
-            # Debug info
-            print(f"Top predictions:")
-            for i, (prob, idx) in enumerate(zip(top_probs, top_indices)):
-                class_name = class_names[idx.item()] if idx.item() < len(class_names) else f"Class_{idx.item()}"
-                print(f"  {i+1}. {class_name}: {prob.item():.3f}")
-            
-        return predicted_class, confidence.item()
+    return predicted_class, confidence.item()
+
         
     except Exception as e:
         st.error(f"Prediction error: {str(e)}")
         return None, None
 
 # UI Code
-st.title("🦋 Butterfly Identifier / Liblikamaja ID")
-st.write("Identify butterflies using your camera or by uploading an image!")
+st.title("🦋  Liblikamaja ID/ Butterfly Identifier")
+st.write("Tuvasta liblikaid oma kaamera abil või laadi üles pilt!/ Identify butterflies using your camera or by uploading an image!")
 
 # Show model info
-if model is not None:
-    st.info(f"📊 Model loaded: {len(class_names)} butterfly species recognized")
+#if model is not None:
+    #st.info(f"📊 Model loaded: {len(class_names)} butterfly species recognized")
 
 # Create tabs for different input methods
 tab1, tab2 = st.tabs(["📷 Live Camera", "📁 Upload Image"])
 
 with tab1:
-    st.header("Camera Capture")
+    st.header("Kaamera jäädvustamine/ Camera Capture")
     st.write("Take a photo of a butterfly for identification!")
     
-    camera_photo = st.camera_input("Take a picture of a butterfly")
+    camera_photo = st.camera_input("Pildista liblikat/ Take a picture of a butterfly")
     
     if camera_photo is not None:
         try:
@@ -328,42 +314,40 @@ with tab1:
             col1, col2 = st.columns(2)
             
             with col1:
-                st.image(image, caption="Captured Image", use_column_width=True)
+                st.image(image, caption="Captured Image/Jäädvustatud pilt", use_column_width=True)
             
             with col2:
-                with st.spinner("Analyzing image..."):
+                with st.spinner("Kujutise analüüsimine..."):
                     predicted_class, confidence = predict_butterfly(image)
                 
-                if predicted_class and confidence:
-                    if confidence >= 0.80:
-                        st.success(f"**Prediction: {predicted_class}**")
-                        st.info(f"Confidence: {confidence:.2%}")
-                        
-                        # Show additional info if available
-                        if predicted_class in butterfly_info:
-                            st.write(f"**About:** {butterfly_info[predicted_class]['description']}")
+                if predicted_class and confidence >= 0.50:
+                    st.success(f"**Liblika: {predicted_class}**")
+                    #st.info(f"Confidence: {confidence:.2%}")
+                else:
+                    st.warning("⚠️ **I don't know what butterfly this is.**")
+                    #st.info(f"Confidence too low: {confidence:.1%}")
+
                     else:
-                        st.warning("⚠️ **Low confidence prediction**")
-                        st.info(f"Best guess: {predicted_class} ({confidence:.1%})")
-                        st.markdown("**Tips for better results:**")
-                        st.markdown("- Use better lighting")
-                        st.markdown("- Get closer to the butterfly")
-                        st.markdown("- Ensure the butterfly is clearly visible")
-                        st.markdown("- Avoid blurry or dark images")
+                        st.warning("⚠️ **Low confidence prediction/ Madala usaldusväärsusega ennustus**")
+                        #st.info(f"Best guess: {predicted_class} ({confidence:.1%})")
+                        st.markdown("**Tips for better results/ Näpunäited paremate tulemuste saavutamiseks:**")
+                        st.markdown("- Use better lighting/ Kasutage paremat valgustust")
+                        st.markdown("- Ensure the butterfly is clearly visible/ Veenduge, et liblikas oleks selgelt nähtav")
+                        st.markdown("- Avoid blurry or dark images/ Vältige uduseid või tumedaid pilte")
                 else:
-                    st.error("Unable to analyze image. Please try again.")
+                    st.error("Unable to analyze image. Please try again./ Pildi analüüsimine ebaõnnestus. Palun proovige uuesti.")
                                     
         except Exception as e:
             st.error(f"Error processing image: {str(e)}")
 
 with tab2:
-    st.header("Upload Image")
-    st.write("Upload a clear photo of a butterfly for identification")
+    st.header("Upload Image/ Laadi pilt üles")
+    st.write("Upload a clear photo of a butterfly for identification/ Laadige üles liblika selge foto tuvastamiseks")
     
     uploaded_file = st.file_uploader(
-        "Choose an image...", 
+        "Choose an image/ Valige pilt...", 
         type=["jpg", "jpeg", "png"],
-        help="Upload a clear photo of a butterfly"
+        help="Upload a clear photo of a butterfly/ Laadi üles selge foto liblikast"
     )
     
     if uploaded_file is not None:
@@ -381,33 +365,32 @@ with tab2:
                     predicted_class, confidence = predict_butterfly(image)
                 
                 if predicted_class and confidence:
-                    if confidence >= 0.80:
-                        st.success(f"**Prediction: {predicted_class}**")
-                        st.info(f"Confidence: {confidence:.2%}")
+                    if confidence >= 0.50:
+                        st.success(f"**liblikas: {predicted_class}**")
+                        #st.info(f"Confidence: {confidence:.2%}")
                         
                         # Show additional info if available
                         if predicted_class in butterfly_info:
                             st.write(f"**About:** {butterfly_info[predicted_class]['description']}")
                     else:
-                        st.warning("⚠️ **Low confidence prediction**")
-                        st.info(f"Best guess: {predicted_class} ({confidence:.1%})")
-                        st.markdown("**Tips for better results:**")
-                        st.markdown("- Use better lighting")
-                        st.markdown("- Get closer to the butterfly")
-                        st.markdown("- Ensure the butterfly is clearly visible")
-                        st.markdown("- Avoid blurry or dark images")
+                        st.warning("⚠️ **Low confidence prediction/ Madala usaldusväärsusega ennustus**")
+                        #st.info(f"Best guess: {predicted_class} ({confidence:.1%})")
+                        st.markdown("**Tips for better results/ Näpunäited paremate tulemuste saavutamiseks:**")
+                        st.markdown("- Use better lighting/ Kasutage paremat valgustust")
+                        st.markdown("- Ensure the butterfly is clearly visible/ Veenduge, et liblikas oleks selgelt nähtav")
+                        st.markdown("- Avoid blurry or dark images/ Vältige uduseid või tumedaid pilte")
                 else:
-                    st.error("Unable to analyze image. Please try again.")
+                    st.error("Unable to analyze image. Please try again./Pildi analüüsimine ebaõnnestus. Palun proovige uuesti.")
                 
         except Exception as e:
             st.error(f"Error processing image: {str(e)}")
 
 # Add footer
 st.markdown("---")
-st.markdown("### How to use:")
-st.markdown("1. **Camera Capture**: Take a photo using your device camera")
-st.markdown("2. **Upload Image**: Choose a butterfly photo from your device")
-st.markdown("3. **Best Results**: Use clear, well-lit photos with the butterfly clearly visible")
+st.markdown("### How to use/ Kuidas kasutada:")
+st.markdown("1. **Camera Capture**: Take a photo using your device camera/ **Kaamera jäädvustamine**: Tehke foto oma seadme kaameraga")
+st.markdown("2. **Upload Image**: Choose a butterfly photo from your device/ **Laadi pilt üles**: Vali oma seadmest liblika foto")
+st.markdown("3. **Best Results**: Use clear, well-lit photos with the butterfly clearly visible/ **Parimad tulemused**: Kasutage selgeid ja hästi valgustatud fotosid, kus liblikas on selgelt nähtav.")
 
 # Debug info (only show if there are issues)
 if st.checkbox("Show debug information"):