Update app.py

app.py

@@ -5,8 +5,8 @@ from PIL import Image
 import numpy as np
 from deepface import DeepFace  # Replacing FER with DeepFace
 from dotenv import load_dotenv
-import time  # For rate limiting
 
+# Print out successful imports
 print("DeepFace is installed and ready to use!")
 print("Google Generative AI module is successfully imported!")
 
@@ -46,10 +46,10 @@ def detect_emotions(image):
 def analyze_emotions_with_llm(emotion, emotions):
     emotion_analysis = f"{emotion}: {emotions[emotion]:.2f}"
     analysis_prompt = f"""
-    As a mental health and emotional well-being expert, analyze the following detected emotions.
-    Detected Emotions:
+    ### As a mental health and emotional well-being expert, analyze the following detected emotions.
+    ### Detected Emotions:
     {emotion_analysis}
-     Analysis Output:
+    ### Analysis Output:
     1. Identify any potential signs of depression based on the detected emotions.
     2. Explain the reasoning behind your identification.
     3. Provide recommendations for addressing any identified issues.
@@ -79,20 +79,20 @@ def display_response_content(response):
 st.title("AI-Powered Depression and Emotion Detection System")
 st.text("Use the AI system for detecting depression and emotions from images.")
 
-# Tabs for different functionalities
-tab1 = st.empty()  # Removing tab for video, leaving only image analysis tab
-
-with tab1:
+# Tabs for different functionalities (only image analysis in this version)
+with st.container():
     st.header("Image Analysis")
     uploaded_file = st.file_uploader("Upload an image for analysis", type=["jpg", "jpeg", "png"], help="Please upload an image file.")
     submit_image = st.button('Analyze Image')
 
     if submit_image:
         if uploaded_file is not None:
-            image = Image.open(uploaded_file)
-            emotion, emotions = detect_emotions(image)
-            if emotion:
-                response = analyze_emotions_with_llm(emotion, emotions)
-                display_response_content(response)
+            image = Image.open(uploaded_file)  # Open the uploaded image
+            emotion, emotions = detect_emotions(image)  # Detect emotions using DeepFace
+            if emotion:  # If emotions are detected
+                response = analyze_emotions_with_llm(emotion, emotions)  # Analyze detected emotions with LLM
+                display_response_content(response)  # Display the analysis response
             else:
-                st.write("No emotions detected in the image.")
+                st.write("No emotions detected in the image.")  # If no emotion is detected
+        else:
+            st.write("Please upload an image first.")  # Prompt for image upload if none is uploaded