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

# 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):
    # Convert image to RGB
    image = image.convert("RGB")
    # Resize 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))
    
    # Use KMeans to find the dominant colors
    kmeans = KMeans(n_clusters=5)
    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

# Function to detect emotions from colors (simplified emotion-color mapping)
def color_emotion_analysis(dominant_colors):
    # Map certain color ranges to emotions
    emotions = []
    for color in dominant_colors:
        # Simple logic: darker tones could indicate sadness
        if np.mean(color) < 85:
            emotions.append("Sadness")
        elif np.mean(color) > 170:
            emotions.append("Happiness")
        else:
            emotions.append("Neutral")
    
    return emotions

# Function to analyze patterns and shapes using OpenCV
def analyze_patterns(image):
    # 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"

# Main function to process image and analyze emotional expression
def analyze_emotion_from_image(image):
    # Analyze colors
    dominant_colors = analyze_colors(image)
    color_emotions = color_emotion_analysis(dominant_colors)
    
    # Analyze patterns
    pattern_analysis = analyze_patterns(image)
    
    return f"Color-based emotions: {color_emotions}\nPattern analysis: {pattern_analysis}"

# 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()