Create app.py

app.py

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+import gradio as gr
+import mne
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load open-source LLM (no training needed)
+model_name = "tiiuae/falcon-7b-instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float16, device_map="auto")
+
+def process_eeg(file):
+    # Load EEG data using MNE
+    raw = mne.io.read_raw_fif(file.name, preload=True)
+    # Compute some features (e.g., average band powers)
+    psd, freqs = mne.time_frequency.psd_welch(raw, fmin=1, fmax=40)
+    alpha_power = compute_band_power(psd, freqs, 8, 12)
+    beta_power = compute_band_power(psd, freqs, 13, 30)
+    # Create a human-readable summary of features
+    data_summary = f"Alpha power: {alpha_power}, Beta power: {beta_power}. The data shows stable alpha rhythms and slightly elevated beta."
+    
+    # Prompt the LLM
+    prompt = f"""You are a neuroscientist analyzing EEG features. 
+    Data Summary: {data_summary}
+
+    Provide a concise, user-friendly interpretation of these findings."""
+    
+    inputs = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(inputs, max_length=200, do_sample=True, top_k=50, top_p=0.95)
+    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return summary
+
+iface = gr.Interface(
+    fn=process_eeg,
+    inputs=gr.File(label="Upload your EEG data (FIF format)"),
+    outputs="text",
+    title="NeuroNarrative-Lite: EEG Summary",
+    description="Upload EEG data to receive a text-based summary from an open-source LLM. No training required!"
+)
+
+if __name__ == "__main__":
+    iface.launch()