app.py

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()