fix errors.

app.py

@@ -4,21 +4,24 @@ import sqlite3
 
 import streamlit as st
 import torch
+import torchvision
 from PIL import Image
 from huggingface_hub import hf_hub_download
 from torchvision import transforms
-from transformers import AutoModelForImageClassification
+from transformers import AutoModelForImageClassification, AutoConfig
 
 # Set the page title
 st.title("Global Bird Classification App")
 
-# Upload an image
-uploaded_file = st.file_uploader("Please select an image", type=["jpg", "jpeg", "png"])
-
 # Input latitude and longitude (optional)
 latitude = st.number_input("Enter latitude (optional)", value=None, format="%f")
 longitude = st.number_input("Enter longitude (optional)", value=None, format="%f")
 
+st.text('Please fill the coordinates before upload image.')
+
+# Upload an image
+uploaded_file = st.file_uploader("Please select an image", type=["jpg", "jpeg", "png"])
+
 lang = st.selectbox(
     "Result Language",
     options=[2, 1, 0],
@@ -102,18 +105,29 @@ GROUP BY d.species, m.cls;
 # If the user uploads an image
 if uploaded_file is not None:
     try:
-        sqlite_path = hf_hub_download(repo_id='sunjiao/osea', filename='avonet.db')
-        st.success(f"Successfully downloaded distribution database from Hugging Face Hub!")
-
         label_map_path = hf_hub_download(repo_id='sunjiao/osea', filename='bird_info.json')
         st.success(f"Successfully downloaded labels from Hugging Face Hub!")
     except Exception as e:
         st.error(f"Failed to download the file: {e}")
         st.stop()
 
-    db = DistributionDB(sqlite_path)
-    species_list = db.get_list(latitude, longitude)
-    db.close()
+    with open(label_map_path, 'r') as f:
+        data = f.read()
+
+    bird_info = json.loads(data)
+
+    species_list = None
+    if latitude and longitude:
+        try:
+            sqlite_path = hf_hub_download(repo_id='sunjiao/osea', filename='avonet.db')
+            st.success(f"Successfully downloaded distribution database from Hugging Face Hub!")
+        except Exception as e:
+            st.error(f"Failed to download the file: {e}")
+            st.stop()
+
+        db = DistributionDB(sqlite_path)
+        species_list = db.get_list(latitude, longitude)
+        db.close()
 
     # Open the image
     image = Image.open(uploaded_file)
@@ -121,17 +135,21 @@ if uploaded_file is not None:
     # Display the uploaded image
     st.image(image, caption="Uploaded Image", use_container_width=True)
 
-    model = AutoModelForImageClassification.from_pretrained('sunjiao/osea')
+    try:
+        weight_dict = hf_hub_download(repo_id='sunjiao/osea', filename='pytorch_model.bin')
+        st.success(f"Successfully downloaded weight dict from Hugging Face Hub!")
+    except Exception as e:
+        st.error(f"Failed to download the file: {e}")
+        st.stop()
+
+    model = torchvision.models.resnet34(num_classes=11000)
+    model.load_state_dict(torch.load(weight_dict, map_location=device))
+    model.eval()
 
     results = classify_objects(model, image, species_list)
 
     top3_results = results[:3]
 
-    with open(label_map_path, 'r') as f:
-        data = f.read()
-
-    bird_info = json.loads(data)
-
     # Display the top 3 results and their probabilities
     st.subheader("Classification Results (Top 3):")
     for result in top3_results:

requirements.txt

@@ -2,8 +2,9 @@ huggingface_hub
 transformers
 ImageHash==4.3.1
 openpyxl==3.1.5
-Pillow==11.0.0
+Pillow
 pyshp==2.3.1
-torch==2.5.0
-torchvision==0.20.0
+streamlit
+torch
+torchvision
 tqdm==4.67.1