Update app.py

app.py

@@ -1,117 +1,117 @@
-import streamlit as st
-import google.generativeai as genai
-import os
-from PIL import Image
-import cv2
-from io import BytesIO
-import base64
-from dotenv import load_dotenv
-import numpy as np
-from fer import FER
-
-load_dotenv()
-
-genai.configure(api_key=("AIzaSyDziGvuT1woHnH4_S3L_zQZV55Yj-123A8"))
-
-# gemini function for general content generation
-def get_gemini_response(input):
-    try:
-        model = genai.GenerativeModel('gemini-pro')
-        response = model.generate_content(input)
-        return response
-    except Exception as e:
-        st.error(f"Error: {e}")
-        return None
-
-# Function to analyze image for depression and emotion detection using FER
-def detect_emotions(image):
-    detector = FER(mtcnn=True)
-    # Convert PIL Image to NumPy array
-    image_np = np.array(image)
-    emotions = detector.detect_emotions(image_np)
-    if emotions:
-        return emotions[0]['emotions']
-    return None
-
-# Function to analyze detected emotions with LLM
-def analyze_emotions_with_llm(emotions):
-    emotion_analysis = ", ".join([f"{emotion}: {score:.2f}" for emotion, score in emotions.items()])
-    
-    analysis_prompt = f"""
-    ### As a mental health and emotional well-being expert, analyze the following detected emotions.
-    ### Detected Emotions:
-    {emotion_analysis}
-    ### 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.
-    """
-    response = get_gemini_response(analysis_prompt)
-    return response
-
-# Function to capture live video frame for analysis
-def capture_video_frame():
-    video_capture = cv2.VideoCapture(0)
-    ret, frame = video_capture.read()
-    if ret:
-        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        return Image.fromarray(frame_rgb)
-    return None
-
-# Function to parse and display response content
-def display_response_content(response):
-    st.subheader("Response Output")
-    if response and response.candidates:
-        response_content = response.candidates[0].content.parts[0].text if response.candidates[0].content.parts else ""
-        sections = response_content.split('###')
-        for section in sections:
-            if section.strip():
-                section_lines = section.split('\n')
-                section_title = section_lines[0].strip()
-                section_body = '\n'.join(line.strip() for line in section_lines[1:] if line.strip())
-                if section_title:
-                    st.markdown(f"**{section_title}**")
-                if section_body:
-                    st.write(section_body)
-    else:
-        st.write("No response received from the model or quota exceeded.")
-
-## Streamlit App
-st.title("AI-Powered Depression and Emotion Detection System")
-st.text("Use the AI system for detecting depression and emotions from images and live video.")
-
-# Tabs for different functionalities
-tab1, tab2 = st.tabs(["Image Analysis", "Live Video Analysis"])
-
-with tab1:
-    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)
-            emotions = detect_emotions(image)
-            if emotions:
-                response = analyze_emotions_with_llm(emotions)
-                # Parse and display response in a structured way
-                display_response_content(response)
-            else:
-                st.write("No emotions detected in the image.")
-
-with tab2:
-    st.header("Live Video Analysis")
-    capture_frame = st.button('Capture and Analyze Frame')
-    
-    if capture_frame:
-        image = capture_video_frame()
-        if image is not None:
-            emotions = detect_emotions(image)
-            if emotions:
-                response = analyze_emotions_with_llm(emotions)
-                # Parse and display response in a structured way
-                display_response_content(response)
-            else:
-                st.write("No emotions detected in the video frame.")
-        else:
-            st.write("Failed to capture video frame.")
+import streamlit as st
+import google.generativeai as genai
+import os
+from PIL import Image
+import cv2
+from io import BytesIO
+import base64
+from dotenv import load_dotenv
+import numpy as np
+from fer import FER
+
+load_dotenv()
+
+genai.configure(api_key=("AIzaSyARLRWGr4vloCAhea-rt8dMV7gWeC1pgBE"))
+
+# gemini function for general content generation
+def get_gemini_response(input):
+    try:
+        model = genai.GenerativeModel('gemini-pro')
+        response = model.generate_content(input)
+        return response
+    except Exception as e:
+        st.error(f"Error: {e}")
+        return None
+
+# Function to analyze image for depression and emotion detection using FER
+def detect_emotions(image):
+    detector = FER(mtcnn=True)
+    # Convert PIL Image to NumPy array
+    image_np = np.array(image)
+    emotions = detector.detect_emotions(image_np)
+    if emotions:
+        return emotions[0]['emotions']
+    return None
+
+# Function to analyze detected emotions with LLM
+def analyze_emotions_with_llm(emotions):
+    emotion_analysis = ", ".join([f"{emotion}: {score:.2f}" for emotion, score in emotions.items()])
+    
+    analysis_prompt = f"""
+    ### As a mental health and emotional well-being expert, analyze the following detected emotions.
+    ### Detected Emotions:
+    {emotion_analysis}
+    ### 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.
+    """
+    response = get_gemini_response(analysis_prompt)
+    return response
+
+# Function to capture live video frame for analysis
+def capture_video_frame():
+    video_capture = cv2.VideoCapture(0)
+    ret, frame = video_capture.read()
+    if ret:
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        return Image.fromarray(frame_rgb)
+    return None
+
+# Function to parse and display response content
+def display_response_content(response):
+    st.subheader("Response Output")
+    if response and response.candidates:
+        response_content = response.candidates[0].content.parts[0].text if response.candidates[0].content.parts else ""
+        sections = response_content.split('###')
+        for section in sections:
+            if section.strip():
+                section_lines = section.split('\n')
+                section_title = section_lines[0].strip()
+                section_body = '\n'.join(line.strip() for line in section_lines[1:] if line.strip())
+                if section_title:
+                    st.markdown(f"**{section_title}**")
+                if section_body:
+                    st.write(section_body)
+    else:
+        st.write("No response received from the model or quota exceeded.")
+
+## Streamlit App
+st.title("AI-Powered Depression and Emotion Detection System")
+st.text("Use the AI system for detecting depression and emotions from images and live video.")
+
+# Tabs for different functionalities
+tab1, tab2 = st.tabs(["Image Analysis", "Live Video Analysis"])
+
+with tab1:
+    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)
+            emotions = detect_emotions(image)
+            if emotions:
+                response = analyze_emotions_with_llm(emotions)
+                # Parse and display response in a structured way
+                display_response_content(response)
+            else:
+                st.write("No emotions detected in the image.")
+
+with tab2:
+    st.header("Live Video Analysis")
+    capture_frame = st.button('Capture and Analyze Frame')
+    
+    if capture_frame:
+        image = capture_video_frame()
+        if image is not None:
+            emotions = detect_emotions(image)
+            if emotions:
+                response = analyze_emotions_with_llm(emotions)
+                # Parse and display response in a structured way
+                display_response_content(response)
+            else:
+                st.write("No emotions detected in the video frame.")
+        else:
+            st.write("Failed to capture video frame.")