add new model

app.py

@@ -10,7 +10,7 @@ import numpy as np
 
 pipe_yolos = pipeline("object-detection", model="hustvl/yolos-tiny")
 pipe_emotions = pipeline("image-classification", model="dima806/facial_emotions_image_detection")
-
+pipe_emotions_refined = pipeline("image-classification", model="felixwf/fine_tuned_face_emotion_model")
 
 
 st.title("Online Teaching Effect Monitor")
@@ -69,33 +69,44 @@ if file_name is not None:
         ax.axis('off')
 
     # 识别每个人的表情
-    output_list = []
+    output_list_emotions = []
+    output_list_emotions_refined = []
     
     for idx, face in enumerate(persons_image_list):
       print(f"processing {idx}")
       output = pipe_emotions(face)
-      output_list.append(output[0])
+      output_list_emotions.append(output[0])
+      output = pipe_emotions_refined(face)
+      output_list_emotions_refined.append(output[0])
     
-    print(output_list)
-    st.text(output_list)
+    print(output_list_emotions)
+    st.text(output_list_emotions)
+    print(output_list_emotions_refined)
+    st.text(output_list_emotions_refined)
 
     
     # 统计各种标签的数量
     label_counts = {}
-    for item in output_list:
+    for item in output_list_emotions:
         label = item['label']
         if label in label_counts:
             label_counts[label] += 1
         else:
             label_counts[label] = 1
-    
+    for item in output_list_emotions_refined:
+        label = item['label']
+        if label in label_counts:
+            label_counts[label] += 1
+        else:
+            label_counts[label] = 1
+
     # 绘制饼状图
     labels = list(label_counts.keys())
     sizes = list(label_counts.values())
     
     pie_fig, ax = plt.subplots()
     ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)
-    ax.set_title('Distribution of Labels')
+    ax.set_title('Distribution of Emotions')
     ax.axis('equal')  # 确保饼状图为圆形
     # plt.show()
     # Use Streamlit columns to display the images and pie chart side by side