Update app.py

app.py

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+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 deepface import DeepFace  # Replacing FER with DeepFace
+print("DeepFace is installed and ready to use!")
+
+
+load_dotenv()
+
+genai.configure(api_key=("AIzaSyBP1kQr-80Aq_K5_9AVgD1MLJqs05Cg20Q"))
+
+# 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 DeepFace
+def detect_emotions(image):
+    try:
+        # Use DeepFace to analyze emotions
+        analysis = DeepFace.analyze(image, actions=['emotion'], enforce_detection=False)
+        # Return the dominant emotion and its score
+        return analysis[0]['dominant_emotion'], analysis[0]['emotion']
+    except Exception as e:
+        st.error(f"Error during emotion detection: {e}")
+        return None, None
+
+# Function to analyze detected emotions with LLM
+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:
+    {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)
+    if not video_capture.isOpened():
+        st.error("Failed to access the webcam. Ensure you have allowed camera access in your browser.")
+        return None
+    ret, frame = video_capture.read()
+    video_capture.release()
+    if ret:
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        return Image.fromarray(frame_rgb)
+    else:
+        st.error("Failed to capture a frame from the webcam.")
+        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)
+            emotion, emotions = detect_emotions(image)
+            if emotion:
+                response = analyze_emotions_with_llm(emotion, 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:
+            emotion, emotions = detect_emotions(image)
+            if emotion:
+                response = analyze_emotions_with_llm(emotion, 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.")