modify prediction method, add webcam and image prediction

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 from streamlit_option_menu import option_menu
 from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
 from prediction import predict_emotion
+import prediction
 import eda
 import model_result
 
@@ -27,7 +28,8 @@ with st.sidebar:
             "Distribution",
             "Image Sample",
             "Model Result",
-            "Classification",
+            "Webcam Classification",
+            "Image Classification"
         ],
         icons=["bar-chart", "link-45deg", "code-square"],
         menu_icon="cast",
@@ -40,5 +42,7 @@ elif selected == "Image Sample":
     eda.samples()
 elif selected == "Model Result":
     model_result.report()
-elif selected == "Classification":
+elif selected == "Webcam Classification":
     main()  # Call the main function for emotion detection
+elif selected == "Image Classification":
+    prediction.image_prediction()

prediction.py

@@ -1,9 +1,13 @@
+import streamlit as st
 import cv2
 import numpy as np
+import matplotlib.pyplot as plt
 from tensorflow.keras.preprocessing import image
+from tensorflow.keras.models import load_model
 from keras.models import load_model
 import pandas as pd
 
+
 # Load pre-trained emotion classification model
 emotion_classification_model = load_model('./model/model_fine_tune.h5')  # Replace with actual path
 
@@ -52,3 +56,90 @@ def predict_emotion(frame):
         cv2.putText(img, f'Emotion: {pred_class_single[0]}', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
 
     return img  # Return the annotated frame
+
+def image_prediction():
+    
+       
+    emotion_classification_model = load_model('./model/model_fine_tune.h5')
+    
+    
+    url = "https://lh3.googleusercontent.com/0e8O0JPOp_ydq7wqv6kgYz6UpF9w_INfnOLIhnJZBEHFcWIygkuLa3SVghhGYgE0XWzQYBPb6wb1eQFN0pVIAYlzEeNojYuCWg=s0"
+    
+    def img_url(url):
+        res = request.urlopen(url).read()
+        img_ori = image.load_img(BytesIO(res))
+        img = image.load_img(BytesIO(res), target_size=(48, 48), keep_aspect_ratio=True)
+        show_predict(img, img_ori)
+        
+    def show_predict(img, img_ori):
+        col1, col2 = st.columns(2)
+        fig = plt.figure()
+        plt.imshow(img_ori)
+        plt.axis('off')
+        col1.pyplot(fig)
+        
+        img_array = image.img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array /= (255*117)  # Normalize the image
+
+        inf_pred_single = emotion_classification_model.predict(img_array)
+        
+        data_inf_single = []
+
+        rank = []
+
+        for i in inf_pred_single[0]:
+            value = i * 100
+            rank.append(value)
+            data_inf_single.append(f'{value.round(2)}%')
+
+        rank = (-np.array(rank)).argsort()[:2]
+        
+        pred_class_single = pd.DataFrame(class_labels).loc[rank][0].tolist()
+
+        prediction_result_single = pd.DataFrame(columns=["angry", "disgusted", "fearful", "happy", "neutral", "sad", "surprised"])
+        prediction_result_single.loc[len(prediction_result_single)] = data_inf_single
+
+        prediction_result_single
+        
+        st.markdown("""
+                    <style>
+                    .big-font {
+                        font-size:30px !important;
+                    }
+                    </style>
+                    """, unsafe_allow_html=True)
+        
+        col2.write('Prediction Class:')
+        col2.markdown(f'<p class="big-font">{pred_class_single[0].capitalize()}</p>', unsafe_allow_html=True)
+        
+        col2.dataframe(prediction_result_single.set_index(prediction_result_single.columns[0]), use_container_width=True)
+    
+    class_labels = ["angry", "disgusted", "fearful", "happy", "neutral", "sad", "surprised"]
+
+    st.write('Insert Image URL Below (Make sure face is centered and fitted)')
+    
+    st.markdown('[Example Image](https://cdn.idntimes.com/content-images/community/2021/12/whatsapp-image-2021-12-02-at-190446-8ecf63e1fa6b5c8c5e9ac43034bc86d3-c563813ea99f16a795ad4c53af10881a_600x400.jpeg)')
+    
+    col1, col2 = st.columns((9,1))
+    url_input = col1.text_input(label="Image Links")
+    
+    st.markdown(
+                """
+            <style>
+            button {
+                height: auto;
+                margin-top: 28px !important;
+                padding-left: 24px !important;
+                padding-right: 24px !important;
+            }
+            </style>
+            """,
+                unsafe_allow_html=True,
+            )
+    pred_button = col2.button(label="Predict")
+    
+    if pred_button:
+        img_url(url_input)
+    else:
+        img_url(url)