Update app.py

app.py

@@ -65,16 +65,21 @@ class AdvancedSensors:
 
     @staticmethod
     def measure_texture(x, y):
-        textures = ["nano-smooth", "quantum-rough", "neuro-bumpy", "plasma-silky", "graviton-grainy", "zero-point-soft", "dark-matter-hard", "bose-einstein-condensate"]
+        textures = [
+            "nano-smooth", "quantum-rough", "neuro-bumpy", "plasma-silky",
+            "graviton-grainy", "zero-point-soft", "dark-matter-hard", "bose-einstein-condensate"
+        ]
         return textures[hash((x, y)) % len(textures)]
 
     @staticmethod
     def measure_em_field(x, y, sensitivity):
-        return (np.sin(x/30) * np.cos(y/30) + np.random.normal(0, 0.1)) * 10 * sensitivity
+        return (np.sin(x / 30) * np.cos(y / 30) + np.random.normal(0, 0.1)) * 10 * sensitivity
 
     @staticmethod
     def measure_quantum_state(x, y):
-        states = ["superposition", "entangled", "decoherent", "quantum tunneling", "quantum oscillation"]
+        states = [
+            "superposition", "entangled", "decoherent", "quantum tunneling", "quantum oscillation"
+        ]
         return states[hash((x, y)) % len(states)]
 
 # Create more detailed sensation map for the avatar
@@ -103,11 +108,15 @@ avatar_sensation_map = create_sensation_map(AVATAR_WIDTH, AVATAR_HEIGHT)
 
 # Create futuristic human-like avatar
 def create_avatar():
-    img = Image.new('RGBA', (AVATAR_WIDTH, AVATAR_HEIGHT), color=(0,0,0,0))
+    img = Image.new('RGBA', (AVATAR_WIDTH, AVATAR_HEIGHT), color=(0, 0, 0, 0))
     draw = ImageDraw.Draw(img)
     
     # Body outline
-    draw.polygon([(300, 100), (200, 250), (250, 600), (300, 750), (350, 600), (400, 250)], fill=(0, 255, 255, 100), outline=(0, 255, 255, 255))
+    draw.polygon(
+        [(300, 100), (200, 250), (250, 600), (300, 750), (350, 600), (400, 250)],
+        fill=(0, 255, 255, 100),
+        outline=(0, 255, 255, 255)
+    )
     
     # Head
     draw.ellipse([250, 50, 350, 150], fill=(0, 255, 255, 100), outline=(0, 255, 255, 255))
@@ -163,14 +172,22 @@ with col2:
     
     if canvas_result.json_data is not None:
         objects = canvas_result.json_data["objects"]
-        if len(objects) > 0:last_touch = objects[-1]
-        touch_x, touch_y = last_touch["left"], last_touch["top"]
+        if len(objects) > 0:
+            last_touch = objects[-1]
+            touch_x, touch_y = last_touch["left"], last_touch["top"]
             
             sensation = avatar_sensation_map[int(touch_y), int(touch_x)]
-            pain, pleasure, pressure_sens, temp_sens, texture_sens, em_sens, tickle_sens, itch_sens, quantum_sens, neural_sens = sensation
+            (
+                pain, pleasure, pressure_sens, temp_sens, texture_sens,
+                em_sens, tickle_sens, itch_sens, quantum_sens, neural_sens
+            ) = sensation
 
-            measured_pressure = AdvancedSensors.measure_pressure(pressure_sens, touch_pressure, touch_duration)
-            measured_temp = AdvancedSensors.measure_temperature(37, touch_pressure, touch_duration)
+            measured_pressure = AdvancedSensors.measure_pressure(
+                pressure_sens, touch_pressure, touch_duration
+            )
+            measured_temp = AdvancedSensors.measure_temperature(
+                37, touch_pressure, touch_duration
+            )
             measured_texture = AdvancedSensors.measure_texture(touch_x, touch_y)
             measured_em = AdvancedSensors.measure_em_field(touch_x, touch_y, em_sens)
             if use_quantum:
@@ -226,7 +243,10 @@ with col2:
             AI:"""
             
             input_ids = tokenizer.encode(prompt, return_tensors="pt")
-            output = model.generate(input_ids, max_length=300, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7)
+            output = model.generate(
+                input_ids, max_length=300, num_return_sequences=1,
+                no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7
+            )
             
             response = tokenizer.decode(output[0], skip_special_tokens=True).split("AI:")[-1].strip()
             
@@ -236,7 +256,10 @@ with col2:
 # Visualize sensation map
 st.subheader("Quantum Neuro-Sensory Map")
 fig, axs = plt.subplots(2, 5, figsize=(20, 8))
-titles = ['Pain', 'Pleasure', 'Pressure', 'Temperature', 'Texture', 'EM Field', 'Tickle', 'Itch', 'Quantum', 'Neural']
+titles = [
+    'Pain', 'Pleasure', 'Pressure', 'Temperature', 'Texture',
+    'EM Field', 'Tickle', 'Itch', 'Quantum', 'Neural'
+]
 
 for i, title in enumerate(titles):
     ax = axs[i // 5, i % 5]
@@ -266,4 +289,4 @@ The AI's responses are generated using an advanced language model, providing det
 # Footer
 st.write("---")
 st.write("NeuraSense AI: Quantum-Enhanced Sensory Simulation v3.0")
-st.write("Disclaimer: This is an advanced simulation and does not represent current technological capabilities.")
+st.write("Disclaimer: This is an advanced simulation and does not represent current technological capabilities.")