import streamlit as st
import google.generativeai as genai
import os
from PIL import Image
import numpy as np
from deepface import DeepFace  # Replacing FER with DeepFace
from dotenv import load_dotenv

# Print out successful imports
print("DeepFace is installed and ready to use!")
print("Google Generative AI module is successfully imported!")

# Load API keys and environment variables
load_dotenv()
genai.configure(api_key="AIzaSyAEzZLb7R1CNTWwFXoUsWNrV47X9JgGu1o")

# 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:
        # Handle quota exceeded error
        if "RATE_LIMIT_EXCEEDED" in str(e):
            st.error("Quota exceeded for content generation. Please try again later.")
            return None
        else:
            st.error(f"Error: {e}")
            return None

# Function to analyze image for depression and emotion detection using DeepFace
def detect_emotions(image):
    try:
        # Convert PIL Image to NumPy array
        image_array = np.array(image)
        # Use DeepFace to analyze emotions
        analysis = DeepFace.analyze(image_array, 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.
    """
    response = get_gemini_response(analysis_prompt)
    return response

# Function to parse and display response content
def display_response_content(response):
    st.subheader("Response Output")
    if response and hasattr(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.")

# 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)  # 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.")  # If no emotion is detected
        else:
            st.write("Please upload an image first.")  #