Update app.py

app.py

@@ -1,12 +1,13 @@
 import streamlit as st
 from PIL import Image
 from transformers import pipeline
+import torch
 
 # Create an image classification pipeline with scores
 pipe = pipeline("image-classification", model="trpakov/vit-face-expression", top_k=None)
 
 # Define emotion labels
-#emotion_labels = ["Neutral", "Sad", "Angry", "Surprised", "Happy"]
+emotion_labels = ["Neutral", "Sad", "Angry", "Surprised", "Happy"]
 
 # Streamlit app
 st.title("Emotion Recognition with vit-face-expression")
@@ -15,39 +16,29 @@ st.title("Emotion Recognition with vit-face-expression")
 x = st.slider('Select a value')
 st.write(f"{x} squared is {x * x}")
 
-# Upload images
-uploaded_images = st.file_uploader("Upload images", type=["jpg", "png"], accept_multiple_files=True)
-
-if st.button("Predict Emotions") and uploaded_images:
-    if len(uploaded_images) == 2:
-        # Open the uploaded images
-        images = [Image.open(img) for img in uploaded_images]
-        file_names = [img.name for img in uploaded_images]  # Get file names
-
-        # Predict emotion for each image using the pipeline
-        results = [pipe(image) for image in images]
-
-        # Display images and predicted emotions side by side
-        col1, col2 = st.columns(2)
-        for i in range(2):
-            predicted_class = results[i][0]["label"]
-            predicted_emotion = predicted_class.split("_")[-1].capitalize()
-            col = col1 if i == 0 else col2
-            col.image(images[i], caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
-            col.write(f"Emotion Scores: {predicted_emotion}: {results[i][0]['score']:.4f}")
-            col.write(f"Original File Name: {file_names[i]}")  # Display original file name
-    else:
-        # Open the uploaded images
-        images = [Image.open(img) for img in uploaded_images]
-
-        # Predict emotion for each image using the pipeline
-        results = [pipe(image) for image in images]
-
-        # Display images and predicted emotions
-        for i, result in enumerate(results):
-            predicted_class = result[0]["label"]
-            predicted_emotion = predicted_class.split("_")[-1].capitalize()
-            st.image(images[i], caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
-            st.write(f"Emotion Scores for #{i+1} Image")
-            st.write(f"{predicted_emotion}: {result[0]['score']:.4f}")
-            st.write(f"Original File Name: {uploaded_images[i].name}")  # Display original file name
+# Upload image
+uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png"])
+
+if uploaded_image:
+    image = Image.open(uploaded_image)
+    # Predict emotion using the pipeline
+    results = pipe(image)
+    predicted_class = results[0]["label"]
+    #p_sc = 
+    predicted_emotion = predicted_class.split("_")[-1].capitalize()
+
+    st.image(image, caption=f"Predicted emotion: {predicted_emotion}", use_column_width=True)
+
+    # Get the keys of results[0]
+    keys = results[0].keys()
+
+    # Display the keys and values of results[0]
+    st.write("Keys and Values of results[0]:")
+    for key, value in results[0].items():
+        st.write(f"Key: {key}, Value: {value}")
+
+    # Display the keys and values of all results
+    for i, result in enumerate(results):
+        st.write(f"Keys and Values of results[{i}]:")
+        for key, value in result.items():
+            st.write(f"Key: {key}, Value: {value}")