app.py

import torch
import cv2
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from transformers import pipeline
import gradio as gr
from sklearn.cluster import KMeans
from colorsys import rgb_to_hsv

# Emotion detection pipeline for text (if any text is included in assets)
emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)

# Function to analyze colors in an image
def analyze_colors(image):
    try:
        # Ensure the image is in RGB format
        if image.mode != "RGB":
            image = image.convert("RGB")

        # Resize the image for faster processing
        image = image.resize((150, 150))

        # Convert to numpy array
        img_array = np.array(image)

        # Reshape image to be a list of pixels
        pixels = img_array.reshape((-1, 3))

        kmeans = KMeans(n_clusters=5, random_state=0)
        kmeans.fit(pixels)
        dominant_colors = kmeans.cluster_centers_

        # Plot the colors for visualization
        plt.figure(figsize=(8, 6))
        plt.imshow([dominant_colors.astype(int)])
        plt.axis('off')
        plt.show()

        return dominant_colors

    except Exception as e:
        print(f"Error in analyze_colors: {e}")
        return None


# Function to analyze emotions based on color (hue, brightness, saturation) and stress with weights
def color_emotion_analysis(dominant_colors):
    try:
        emotions = []
        stress_levels = []
        
        # Weight coefficients for each factor
        brightness_weight = 0.5
        hue_weight = 0.3
        saturation_weight = 0.2

        for color in dominant_colors:
            # Normalize RGB values to 0-1 range
            r, g, b = color / 255.0

            # Convert RGB to HSV
            h, s, v = rgb_to_hsv(r, g, b)  # Hue, Saturation, Value (brightness)

            # Calculate weighted emotion and stress levels
            weighted_brightness = v * brightness_weight
            weighted_hue = h * hue_weight
            weighted_saturation = s * saturation_weight

            # Combine weighted factors
            score = weighted_brightness + weighted_hue + weighted_saturation

            # Analyze emotion and stress based on combined score
            if score < 0.3:  # Lower combined score, less rigid "high stress"
                emotions.append("Sadness")
                stress_levels.append("Moderate-High Stress")
            elif 0.3 <= score < 0.5:
                emotions.append("Neutral")
                stress_levels.append("Moderate Stress")
            elif 0.5 <= score < 0.7:
                emotions.append("Okay")
                stress_levels.append("Low Stress")
            elif 0.7 <= score < 0.85:
                emotions.append("Happiness")
                stress_levels.append("Very Low Stress")
            else:
                emotions.append("Very Happy")
                stress_levels.append("No Stress")
        
        return emotions, stress_levels

    except Exception as e:
        print(f"Error in color_emotion_analysis: {e}")
        return ["Error analyzing emotions"], ["Error analyzing stress levels"]


# Function to analyze patterns and shapes using OpenCV
def analyze_patterns(image):
    try:
        # Convert to grayscale for edge detection
        gray_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
        edges = cv2.Canny(gray_image, 100, 200)

        # Calculate the number of edges (chaos metric)
        num_edges = np.sum(edges > 0)

        if num_edges > 10000:  # Arbitrary threshold for "chaos"
            return "Chaotic patterns - possibly distress"
        else:
            return "Orderly patterns - possibly calm"
    except Exception as e:
        print(f"Error in analyze_patterns: {e}")
        return "Error analyzing patterns"

# Main function to process image and analyze emotional expression
def analyze_emotion_from_image(image):
    try:
        # Ensure the input image is a PIL image
        if isinstance(image, np.ndarray):
            image = Image.fromarray(image)  # Convert to PIL Image if it's a NumPy array

        # Analyze colors
        dominant_colors = analyze_colors(image)
        if dominant_colors is None:
            return "Error analyzing colors"
        
        color_emotions, stress_levels = color_emotion_analysis(dominant_colors)

        # Analyze patterns
        pattern_analysis = analyze_patterns(image)

        return f"Color-based emotions: {color_emotions}\nStress levels: {stress_levels}\nPattern analysis: {pattern_analysis}"
    except Exception as e:
        return f"Error processing image: {str(e)}"


# Gradio interface to upload image files and perform analysis
iface = gr.Interface(fn=analyze_emotion_from_image, inputs="image", outputs="text")

# Launch the interface
if __name__ == "__main__":
    iface.launch()