Create app.py

app.py

@@ -0,0 +1,126 @@
+import streamlit as st
+from PIL import Image
+from transformers import pipeline
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from pandas.plotting import parallel_coordinates
+
+# Initialize session state for results, image names, and image sizes if not already present
+if 'results' not in st.session_state:
+    st.session_state['results'] = []
+if 'image_names' not in st.session_state:
+    st.session_state['image_names'] = []
+if 'image_sizes' not in st.session_state:
+    st.session_state['image_sizes'] = []
+
+# Disable PyplotGlobalUseWarning
+st.set_option('deprecation.showPyplotGlobalUse', False)
+
+# Create an image classification pipeline with scores
+pipe = pipeline("image-classification", model="trpakov/vit-face-expression", top_k=None)
+
+# Streamlit app
+st.title("Emotion Recognition with vit-face-expression")
+
+# Upload images
+uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)
+
+# Display thumbnail images alongside file names and sizes in the sidebar
+selected_images = []
+if uploaded_images:
+    # Reset the image names and sizes lists each time new images are uploaded
+    st.session_state['image_names'] = [img.name for img in uploaded_images]
+    st.session_state['image_sizes'] = [round(img.size / 1024.0, 1) for img in uploaded_images]
+
+    # Add a "Select All" checkbox in the sidebar
+    select_all = st.sidebar.checkbox("Select All", False)
+    
+    for idx, img in enumerate(uploaded_images):
+        image = Image.open(img)
+        checkbox_key = f"{img.name}_checkbox_{idx}"  # Unique key for each checkbox
+        # Display thumbnail image and checkbox in sidebar
+        st.sidebar.image(image, caption=f"{img.name} {img.size / 1024.0:.1f} KB", width=40)
+        selected = st.sidebar.checkbox(f"Select {img.name}", value=select_all, key=checkbox_key)         
+        
+        if selected:
+            selected_images.append(image)
+
+if st.button("Predict Emotions") and selected_images:
+    # Predict emotion for each selected image using the pipeline
+    st.session_state['results'] = [pipe(image) for image in selected_images]
+
+# Generate DataFrame from results
+if st.button("Generate HeatMap & DataFrame"):
+    # Access the results, image names, and sizes from the session state
+    results = st.session_state['results']
+    image_names = st.session_state['image_names']
+    image_sizes = st.session_state['image_sizes']
+    if results:
+        # Initialize an empty list to store all the data
+        data = []
+
+        # Iterate over the results and populate the list with dictionaries
+        for i, result_set in enumerate(results):
+            # Initialize a dictionary for the current set with zeros
+            current_data = {
+            
+                'Happy': 0,
+                'Surprise': 0,
+                'Neutral': 0,
+                'Sad': 0,
+                'Disgust': 0,
+                'Angry': 0,
+                'Fear': 0,
+
+                
+
+                # Add other emotions if necessary
+                'Image Name': image_names[i],
+                #'Image Size (KB)': image_sizes[i]
+                'Image Size (KB)': f"{image_sizes[i]:.1f}"  # Format the size to one decimal place
+            }
+            
+            for result in result_set:
+                # Capitalize the label and update the score in the current set
+                emotion = result['label'].capitalize()
+                score = round(result['score'], 4)  # Round the score to 4 decimal places
+                current_data[emotion] = score
+            
+            # Append the current data to the data list
+            data.append(current_data)
+
+        # Convert the list of dictionaries into a pandas DataFrame
+        df_emotions = pd.DataFrame(data)
+
+        # Display the DataFrame
+        st.write(df_emotions)
+
+        # Plotting the heatmap for the first seven columns
+        plt.figure(figsize=(10, 10))
+        sns.heatmap(df_emotions.iloc[:, :7], annot=True, fmt=".1f", cmap='viridis')
+        plt.title('Heatmap of Emotion Scores')
+        plt.xlabel('Emotion Categories')
+        plt.ylabel('Data Points')
+        st.pyplot(plt)
+        
+
+        
+        # Optional: Save the DataFrame to a CSV file
+        df_emotions.to_csv('emotion_scores.csv', index=False)
+        st.success('DataFrame generated and saved as emotion_scores.csv')
+        
+        with open('emotion_scores.csv', 'r') as f:
+            csv_file = f.read()
+        
+        st.download_button(
+            label='Download Emotion Scores as CSV',
+            data=csv_file,
+            file_name='emotion_scores.csv',
+            mime='text/csv',
+        )
+        
+        st.success('DataFrame generated and available for download.')
+        
+    else:
+        st.error("No results to generate DataFrame. Please predict emotions first.")