Update app.py

app.py

@@ -1,25 +1,82 @@
-import gradio as gr
-from transformers import VisionEncoderDecoderModel, AutoTokenizer
-from PIL import Image
-import io
 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
 
-# Load model and tokenizer
-model = VisionEncoderDecoderModel.from_pretrained("liuhaotian/LLaVA-1.5-7b")
-tokenizer = AutoTokenizer.from_pretrained("liuhaotian/LLaVA-1.5-7b")
+# 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 the image
-def analyze_image(image_blob):
-    image = Image.open(io.BytesIO(image_blob))
-    pixel_values = torch.tensor(image).unsqueeze(0)  # Add batch dimension
-    inputs = tokenizer("Analyze the emotions in this image", return_tensors="pt")
+# 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)
     
-    # Run the model
-    outputs = model.generate(**inputs, pixel_values=pixel_values)
-    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Calculate the number of edges (chaos metric)
+    num_edges = np.sum(edges > 0)
     
-    return result
+    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")
 
-# Set up the Gradio interface
-iface = gr.Interface(fn=analyze_image, inputs="file", outputs="text")
-iface.launch()
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()