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import torch |
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import cv2 |
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from PIL import Image |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from transformers import pipeline |
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import gradio as gr |
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from sklearn.cluster import KMeans |
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from colorsys import rgb_to_hsv |
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from huggingface_hub import InferenceClient |
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emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True) |
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client = InferenceClient("nehapasricha94/LLaVA-image-analysis") |
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def analyze_colors(image): |
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try: |
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if image.mode != "RGB": |
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image = image.convert("RGB") |
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image = image.resize((150, 150)) |
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img_array = np.array(image) |
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pixels = img_array.reshape((-1, 3)) |
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kmeans = KMeans(n_clusters=5, random_state=0) |
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kmeans.fit(pixels) |
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dominant_colors = kmeans.cluster_centers_ |
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plt.figure(figsize=(8, 6)) |
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plt.imshow([dominant_colors.astype(int)]) |
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plt.axis('off') |
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plt.show() |
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return dominant_colors |
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except Exception as e: |
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print(f"Error in analyze_colors: {e}") |
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return None |
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def color_emotion_analysis(dominant_colors): |
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try: |
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emotions = [] |
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stress_levels = [] |
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brightness_weight = 0.5 |
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hue_weight = 0.3 |
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saturation_weight = 0.2 |
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for color in dominant_colors: |
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r, g, b = color / 255.0 |
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h, s, v = rgb_to_hsv(r, g, b) |
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weighted_brightness = v * brightness_weight |
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weighted_hue = h * hue_weight |
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weighted_saturation = s * saturation_weight |
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score = weighted_brightness + weighted_hue + weighted_saturation |
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if score < 0.3: |
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emotions.append("Sadness") |
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stress_levels.append("Moderate-High Stress") |
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elif 0.3 <= score < 0.5: |
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emotions.append("Neutral") |
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stress_levels.append("Moderate Stress") |
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elif 0.5 <= score < 0.7: |
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emotions.append("Okay") |
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stress_levels.append("Low Stress") |
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elif 0.7 <= score < 0.85: |
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emotions.append("Happiness") |
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stress_levels.append("Very Low Stress") |
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else: |
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emotions.append("Very Happy") |
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stress_levels.append("No Stress") |
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return emotions, stress_levels |
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except Exception as e: |
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print(f"Error in color_emotion_analysis: {e}") |
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return ["Error analyzing emotions"], ["Error analyzing stress levels"] |
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def analyze_patterns(image): |
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try: |
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gray_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY) |
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edges = cv2.Canny(gray_image, 100, 200) |
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num_edges = np.sum(edges > 0) |
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if num_edges > 10000: |
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return "Chaotic patterns - possibly distress", 0.8 |
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else: |
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return "Orderly patterns - possibly calm", 0.2 |
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except Exception as e: |
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print(f"Error in analyze_patterns: {e}") |
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return "Error analyzing patterns", 0.5 |
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def compute_overall_result(color_emotions, stress_levels, pattern_analysis, pattern_stress): |
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try: |
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color_emotion_weight = 0.5 |
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pattern_weight = 0.5 |
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dominant_emotion = max(set(color_emotions), key=color_emotions.count) |
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stress_mapping = { |
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"No Stress": 0.1, |
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"Very Low Stress": 0.3, |
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"Low Stress": 0.5, |
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"Moderate Stress": 0.7, |
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"Moderate-High Stress": 0.9, |
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} |
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color_stress_numeric = [stress_mapping[stress] for stress in stress_levels if stress in stress_mapping] |
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avg_color_stress = np.mean(color_stress_numeric) if color_stress_numeric else 0.5 |
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overall_stress = (avg_color_stress * color_emotion_weight) + (pattern_stress * pattern_weight) |
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if overall_stress < 0.3: |
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overall_emotion = "Calm and Relaxed" |
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elif 0.3 <= overall_stress < 0.6: |
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overall_emotion = "Neutral Mood" |
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elif 0.6 <= overall_stress < 0.8: |
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overall_emotion = "Slightly Stressed" |
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else: |
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overall_emotion = "Highly Stressed" |
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return f"Overall emotion: {overall_emotion} (Dominant Color Emotion: {dominant_emotion}, Pattern Analysis: {pattern_analysis})" |
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except Exception as e: |
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return f"Error computing overall result: {str(e)}" |
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def analyze_emotion_from_text(text): |
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try: |
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emotion_scores = emotion_classifier(text) |
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dominant_emotion = max(emotion_scores, key=lambda x: x['score']) |
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return f"Detected emotion from text: {dominant_emotion['label']} with score: {dominant_emotion['score']:.2f}" |
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except Exception as e: |
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print(f"Error analyzing emotion from text: {e}") |
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return "Error analyzing text emotion" |
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def analyze_emotion_from_image(image): |
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print(f"Color emotions: abc") |
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try: |
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print(f"Initial input type: {type(image)}") |
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if isinstance(image, dict) and "path" in image: |
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image = Image.open(requests.get(image["path"], stream=True).raw) |
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print("Loaded image from URL.") |
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elif isinstance(image, np.ndarray): |
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image = Image.fromarray(image) |
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print("Converted image from NumPy array.") |
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print(f"Image size: {image.size}, mode: {image.mode}") |
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dominant_colors = analyze_colors(image) |
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if dominant_colors is None: |
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return "Error analyzing colors" |
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color_emotions, stress_levels = color_emotion_analysis(dominant_colors) |
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print(f"Color emotions: {color_emotions}, Stress levels: {stress_levels}") |
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pattern_analysis, pattern_stress = analyze_patterns(image) |
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print(f"Pattern analysis: {pattern_analysis}, Pattern stress: {pattern_stress}") |
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overall_result = compute_overall_result(color_emotions, stress_levels, pattern_analysis, pattern_stress) |
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return overall_result |
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except Exception as e: |
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print(f"Error processing image: {str(e)}") |
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return f"Error processing image: {str(e)}" |
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iface = gr.Interface(fn=analyze_emotion_from_image, inputs="image", outputs="text") |
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if __name__ == "__main__": |
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iface.launch() |
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