attempt 1

app.py

@@ -3,10 +3,13 @@ import streamlit as st
 from beluga import load_model, process_emotions, generate_prompt
 from emodeepface import check_image_rotation, process_photo
 
-# Title webpage
+# begin loading beluga model and tokenizer
+model, tokenizer = load_model()
+
+# title webpage
 st.title("Affective Journaling Assistant")
 
-# User instructions
+# provide user instructions
 st.write("""
 Welcome to the Affective Journaling Assistant!
 
@@ -20,5 +23,20 @@ To proceed:
 Let's get started!
 """)
 
-# User image upload
+# request user image upload
 file_name = st.file_uploader("Please upload your photo.")
+
+# once an image has been uploaded
+if file_name is not None:
+    # capture image with intended rotation
+    image = check_image_rotation(file_name)
+    # display the image directly
+    st.image(image, use_column_width=True)
+    # process uploaded image
+    emotion_predictions = process_photo(file_name) 
+    # process emotion predictions
+    result = process_emotions(model, tokenizer, emotion_predictions)
+    # generate affective journaling prompt based on emotion predictions
+    prompt = generate_prompt(result)
+    # display journal prompt
+    st.write(prompt)   

emodeepface.py

@@ -32,24 +32,23 @@ def process_photo(file_name):
     backends = ['opencv', 'mtcnn', 'retinaface', 'mediapipe', 'ssd']
     attempt = 0
 
-    if file_name is not None:
-        image = check_image_rotation(file_name)
-        image_data = np.array(image)
+    image = check_image_rotation(file_name)
+    image_data = np.array(image)
 
-        while attempt < len(backends):
-            try:
-                predictions = DeepFace.analyze(image_data, actions=['emotion'], detector_backend=backends[attempt])
-                if len(predictions) > 1:
-                    faces = [(face, face['region']['w'] * face['region']['h']) for face in predictions]
-                    new_predictions = sorted(faces, key=lambda x: x[1], reverse=True)[0][0]
-                    emotion_dict = new_predictions['emotion']
-                    return emotion_dict
-                return predictions['emotion']
-            except Exception as e:
-                if attempt == len(backends) - 1:
-                    error_message = f"Failed to analyze image after attempting all detector backends available. Please upload a new image."
-                    raise ImageProcessingError(error_message)
-                else:
-                    # log the error message for each failed backend here:
-                    print(f"Retrying with backend `{backends[attempt+1]}` due to error: {str(e)}")
-                    attempt += 1
+    while attempt < len(backends):
+        try:
+            predictions = DeepFace.analyze(image_data, actions=['emotion'], detector_backend=backends[attempt])
+            if len(predictions) > 1:
+                faces = [(face, face['region']['w'] * face['region']['h']) for face in predictions]
+                new_predictions = sorted(faces, key=lambda x: x[1], reverse=True)[0][0]
+                emotion_dict = new_predictions['emotion']
+                return emotion_dict
+            return predictions['emotion']
+        except Exception as e:
+            if attempt == len(backends) - 1:
+                error_message = f"Failed to analyze image after attempting all detector backends available. Please upload a new image."
+                raise ImageProcessingError(error_message)
+            else:
+                # log the error message for each failed backend here:
+                print(f"Retrying with backend `{backends[attempt+1]}` due to error: {str(e)}")
+                attempt += 1