Update app.py

app.py

@@ -401,64 +401,122 @@ class NeuralNetworkSimulator:
 
 
 
-# Create more detailed sensation map for the avatar
-def create_sensation_map(width, height):
-   sensation_map = np.zeros((height, width, 12))  # pain, pleasure, pressure, temp, texture, em, tickle, itch, quantum, neural, proprioception, synesthesia
-   for y in range(height):
-       for x in range(width):
-           base_sensitivities = np.random.rand(12) * 0.5 + 0.5
-          
-           # Enhance certain areas
-           if 250 < x < 350 and 50 < y < 150:  # Head
-               base_sensitivities *= 1.5
-           elif 275 < x < 325 and 80 < y < 120:  # Eyes
-               base_sensitivities[0] *= 2  # More sensitive to pain
-           elif 290 < x < 310 and 100 < y < 120:  # Nose
-               base_sensitivities[4] *= 2  # More sensitive to texture
-           elif 280 < x < 320 and 120 < y < 140:  # Mouth
-               base_sensitivities[1] *= 2  # More sensitive to pleasure
-           elif 250 < x < 350 and 250 < y < 550:  # Torso
-               base_sensitivities[2:6] *= 1.3  # Enhance pressure, temp, texture, em
-           elif (150 < x < 250 or 350 < x < 450) and 250 < y < 600:  # Arms
-               base_sensitivities[0:2] *= 1.2  # Enhance pain and pleasure
-           elif 200 < x < 400 and 600 < y < 800:  # Legs
-               base_sensitivities[6:8] *= 1.4  # Enhance tickle and itch
-           elif (140 < x < 160 or 440 < x < 460) and 390 < y < 410:  # Hands
-               base_sensitivities *= 2  # Highly sensitive overall
-           elif (220 < x < 240 or 360 < x < 380) and 770 < y < 790:  # Feet
-               base_sensitivities[6] *= 2  # Very ticklish
-          
-           sensation_map[y, x] = base_sensitivities
-  
-   return sensation_map
+# Set up MediaPipe Pose
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose(static_image_mode=True, min_detection_confidence=0.5)
 
+def detect_humanoid(image):
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    results = pose.process(image_rgb)
+    
+    if results.pose_landmarks:
+        landmarks = results.pose_landmarks.landmark
+        image_height, image_width, _ = image.shape
+        keypoints = []
+        for landmark in landmarks:
+            x = int(landmark.x * image_width)
+            y = int(landmark.y * image_height)
+            keypoints.append((x, y))
+        return keypoints
+    return []
+
+def apply_touch_points(image, keypoints):
+    draw = ImageDraw.Draw(image)
+    for point in keypoints:
+        draw.ellipse([point[0]-5, point[1]-5, point[0]+5, point[1]+5], fill='red')
+    return image
+
+def create_sensation_map(width, height, keypoints):
+    sensation_map = np.zeros((height, width, 12))
+    for y in range(height):
+        for x in range(width):
+            base_sensitivities = np.random.rand(12) * 0.5 + 0.5
+            
+            # Enhance sensitivities near keypoints
+            for kp in keypoints:
+                distance = np.sqrt((x - kp[0])**2 + (y - kp[1])**2)
+                if distance < 30:  # Adjust this value to change the area of influence
+                    base_sensitivities *= 1.5
+            
+            sensation_map[y, x, 0] = base_sensitivities[0] * np.random.rand()  # Pain
+            sensation_map[y, x, 1] = base_sensitivities[1] * np.random.rand()  # Pleasure
+            sensation_map[y, x, 2] = base_sensitivities[2] * np.random.rand()  # Pressure
+            sensation_map[y, x, 3] = base_sensitivities[3] * (np.random.rand() * 10 + 30)  # Temperature
+            sensation_map[y, x, 4] = base_sensitivities[4] * np.random.rand()  # Texture
+            sensation_map[y, x, 5] = base_sensitivities[5] * np.random.rand()  # EM field
+            sensation_map[y, x, 6] = base_sensitivities[6] * np.random.rand()  # Tickle
+            sensation_map[y, x, 7] = base_sensitivities[7] * np.random.rand()  # Itch
+            sensation_map[y, x, 8] = base_sensitivities[8] * np.random.rand()  # Quantum
+            sensation_map[y, x, 9] = base_sensitivities[9] * np.random.rand()  # Neural
+            sensation_map[y, x, 10] = base_sensitivities[10] * np.random.rand()  # Proprioception
+            sensation_map[y, x, 11] = base_sensitivities[11] * np.random.rand()  # Synesthesia
+
+    return sensation_map
+
+def create_heatmap(sensation_map, sensation_type):
+    plt.figure(figsize=(10, 15))
+    sns.heatmap(sensation_map[:, :, sensation_type], cmap='viridis')
+     plt.title(f'{["Pain", "Pleasure", "Pressure", "Temperature", "Texture", "EM Field", "Tickle", "Itch", "Quantum", "Neural", "Proprioception", "Synesthesia"][sensation_type]} Sensation Map')
+    plt.axis('off')
+    
+    buf = io.BytesIO()
+    plt.savefig(buf, format='png')
+    buf.seek(0)
+    
+    data = base64.b64encode(buf.getvalue()).decode('utf-8')
+    
+    plt.close()
+    
+    return f'data:image/png;base64,{data}'
 
+# Streamlit app
+st.title("NeuraSense AI - Humanoid Touch Point Detection")
 
-avatar_sensation_map = create_sensation_map(AVATAR_WIDTH, AVATAR_HEIGHT)
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
-# Create heatmap function
-def create_heatmap(sensation_map):
-    # Calculate the overall sensitivity by taking the mean across all sensations
-    overall_sensitivity = np.mean(sensation_map, axis=2)
+if uploaded_file is not None:
+    # Read the image
+    image = Image.open(uploaded_file)
+    image_np = np.array(image)
     
-    # Create a figure and axis
-    fig, ax = plt.subplots(figsize=(10, 15))
+    # Detect humanoid keypoints
+    keypoints = detect_humanoid(image_np)
     
-    # Generate the heatmap
-    sns.heatmap(overall_sensitivity, cmap='YlOrRd', alpha=0.7, cbar=False, ax=ax)
+    # Apply touch points to the image
+    processed_image = apply_touch_points(image.copy(), keypoints)
     
-    # Remove axes and labels
-    ax.set_axis_off()
+    # Display the processed image
+    st.image(processed_image, caption='Processed Image with Touch Points', use_column_width=True)
     
-    # Convert the plot to an image
-    buf = BytesIO()
-    plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
-    buf.seek(0)
-    heatmap_img = Image.open(buf)
+    # Create sensation map
+    sensation_map = create_sensation_map(image.width, image.height, keypoints)
+    
+    # Display heatmaps for different sensations
+    sensation_types = ["Pain", "Pleasure", "Pressure", "Temperature", "Texture", "EM Field", 
+                       "Tickle", "Itch", "Quantum", "Neural", "Proprioception", "Synesthesia"]
+    
+    selected_sensation = st.selectbox("Select a sensation to view:", sensation_types)
+    heatmap = create_heatmap(sensation_map, sensation_types.index(selected_sensation))
+    st.image(heatmap, use_column_width=True)
+
+    # Generate AI response based on the image and sensations
+    if st.button("Generate AI Response"):
+        # You can customize this part to generate more specific responses based on the detected keypoints and sensations
+        response = generate_ai_response(keypoints, sensation_map)
+        st.write("AI Response:", response)
+
+def generate_ai_response(keypoints, sensation_map):
+    # This is a simple example. You can make this more sophisticated based on your needs.
+    num_keypoints = len(keypoints)
+    avg_sensations = np.mean(sensation_map, axis=(0, 1))
     
-    plt.close(fig)
+    response = f"I detect {num_keypoints} key points on the humanoid figure. "
+    response += "The average sensations across the body are:\n"
+    for i, sensation in enumerate(["Pain", "Pleasure", "Pressure", "Temperature", "Texture", "EM Field", 
+                                   "Tickle", "Itch", "Quantum", "Neural", "Proprioception", "Synesthesia"]):
+        response += f"{sensation}: {avg_sensations[i]:.2f}\n"
     
-    return heatmap_img
+    return response
 
 
 # Create futuristic human-like avatar