File size: 2,297 Bytes
18b850d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import torch
import torch.nn as nn
import torch.optim as optim
import gradio as gr
import numpy as np
# =========================
# AI Model Definition (CNN for Brain-Eye Sync)
# =========================
class BrainEyeCNN(nn.Module):
def __init__(self):
super(BrainEyeCNN, self).__init__()
self.conv1 = nn.Conv1d(in_channels=1, out_channels=8, kernel_size=3, stride=1, padding=1)
self.relu = nn.ReLU()
self.fc1 = nn.Linear(8 * 5, 5) # Fully connected layer
def forward(self, x):
x = x.unsqueeze(1) # Add channel dimension
x = self.relu(self.conv1(x))
x = x.view(x.size(0), -1) # Flatten
x = self.fc1(x)
return x
# Initialize Model
model = BrainEyeCNN()
# =========================
# Pre-trained Simulated Weights
# =========================
# Normally, you'd train & save, but here we load fixed weights for Hugging Face
with torch.no_grad():
model.fc1.weight.fill_(0.5)
model.fc1.bias.fill_(0.1)
# =========================
# Quantum-Inspired Prediction
# =========================
def quantum_predict(data):
"""
Quantum-Inspired Prediction: Uses parallel thought processing
"""
q_data = torch.tensor([data], dtype=torch.float32)
ai_output = model(q_data)
prediction = torch.argmax(ai_output, dim=1).item()
# Quantum Parallelism Simulation
quantum_states = ["Relaxed", "Focused", "Anxiety", "Meditative", "Decision-Making"]
return quantum_states[prediction]
# =========================
# Gradio UI for Hugging Face
# =========================
def predict_live(breathing, heartbeat, eye_focus, memory, cognition):
input_data = [float(breathing), float(heartbeat), float(eye_focus), float(memory), float(cognition)]
prediction = quantum_predict(input_data)
return f"Predicted Mental State: {prediction}"
# Deploy on Hugging Face
interface = gr.Interface(
fn=predict_live,
inputs=[
gr.Textbox(label="Breathing Rate (0-1)"),
gr.Textbox(label="Heart Rate (bpm)"),
gr.Textbox(label="Eye Focus Level (0-1)"),
gr.Textbox(label="Memory Recall Strength (0-1)"),
gr.Textbox(label="Cognitive Load (0-1)")
],
outputs="text"
)
# Run the Gradio app
if __name__ == "__main__":
interface.launch()
|