Create app.py
Browse files
app.py
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import gradio as gr
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import numpy as np
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# =========================
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# AI Model Definition (CNN for Brain-Eye Sync)
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# =========================
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class BrainEyeCNN(nn.Module):
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def __init__(self):
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super(BrainEyeCNN, self).__init__()
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self.conv1 = nn.Conv1d(in_channels=1, out_channels=8, kernel_size=3, stride=1, padding=1)
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self.relu = nn.ReLU()
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self.fc1 = nn.Linear(8 * 5, 5) # Fully connected layer
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def forward(self, x):
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x = x.unsqueeze(1) # Add channel dimension
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x = self.relu(self.conv1(x))
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x = x.view(x.size(0), -1) # Flatten
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x = self.fc1(x)
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return x
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# Initialize Model
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model = BrainEyeCNN()
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# =========================
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# Pre-trained Simulated Weights
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# =========================
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# Normally, you'd train & save, but here we load fixed weights for Hugging Face
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with torch.no_grad():
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model.fc1.weight.fill_(0.5)
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model.fc1.bias.fill_(0.1)
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# =========================
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# Quantum-Inspired Prediction
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# =========================
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def quantum_predict(data):
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"""
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Quantum-Inspired Prediction: Uses parallel thought processing
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"""
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q_data = torch.tensor([data], dtype=torch.float32)
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ai_output = model(q_data)
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prediction = torch.argmax(ai_output, dim=1).item()
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# Quantum Parallelism Simulation
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quantum_states = ["Relaxed", "Focused", "Anxiety", "Meditative", "Decision-Making"]
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return quantum_states[prediction]
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# =========================
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# Gradio UI for Hugging Face
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# =========================
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def predict_live(breathing, heartbeat, eye_focus, memory, cognition):
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input_data = [float(breathing), float(heartbeat), float(eye_focus), float(memory), float(cognition)]
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prediction = quantum_predict(input_data)
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return f"Predicted Mental State: {prediction}"
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# Deploy on Hugging Face
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interface = gr.Interface(
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fn=predict_live,
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inputs=[
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gr.Textbox(label="Breathing Rate (0-1)"),
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gr.Textbox(label="Heart Rate (bpm)"),
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gr.Textbox(label="Eye Focus Level (0-1)"),
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gr.Textbox(label="Memory Recall Strength (0-1)"),
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gr.Textbox(label="Cognitive Load (0-1)")
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],
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outputs="text"
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)
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# Run the Gradio app
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if __name__ == "__main__":
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interface.launch()
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