Update app.py

app.py

@@ -1,75 +1,135 @@
-# Installation
-# Clone the repository
-#git clone https://github.com/NASA-IMPACT/Surya.git
-#cd Surya
-
-# Install dependencies (using uv as recommended, or use pip with requirements.txt if available)
-#curl -LsSf https://astral.sh/uv/install.sh | sh
-#source ~/.bashrc
-#uv sync
-#source .venv/bin/activate
-
-# Alternatively, if using pip:
-# pip install -r requirements.txt  # Assuming the repo has this file
-
-# Usage Example: Load the model and perform zero-shot forecasting
-#import os
-#os.system("pip freeze")
-#os.system("python -v")
-#os.system("pip install git+https://github.com/NASA-IMPACT/Surya.git")
-
+import gradio as gr
 import torch
 from huggingface_hub import hf_hub_download
-from surya.model import Surya  # Adjust import based on actual module/class name in repo (likely surya.model or similar)
-
-# Download pretrained weights from Hugging Face
-checkpoint_path = hf_hub_download(
-    repo_id="nasa-ibm-ai4science/Surya-1.0",
-    filename="surya.366m.v1.pt"  # Adjust filename based on actual weights file in the repo
-)
-
-# Initialize the model (parameters inferred from architecture description)
-model = Surya(
-    img_size=4096,          # Native resolution 4096x4096
-    patch_size=16,          # Patch size 16x16, resulting in 65,536 tokens
-    in_chans=13,            # 8 AIA channels + 5 HMI products
-    embed_dim=1280,         # Internal dimension
-    spectral_blocks=2,      # Two spectral gating blocks
-    attention_blocks=8,     # Eight long-short attention layers
-    # Additional params like mlp_ratio=4, norm_layer=torch.nn.LayerNorm, etc., as needed
-)
-
-# Load weights
-model.load_state_dict(torch.load(checkpoint_path, map_location="cpu"))
-model.eval()  # Set to evaluation mode for inference
-
-# Prepare input data (example: batch of multi-instrument SDO data)
-# Input shape: [batch_size, channels=13, time_steps, height=4096, width=4096]
-# Preprocess data as per the paper: alignment, normalization (scaled signum-log transform)
-input_tensor = torch.randn(1, 13, 5, 4096, 4096)  # Dummy input: 1 batch, 13 channels, 5 time steps
-
-# Perform inference (e.g., predict 60 minutes ahead)
-with torch.no_grad():
-    prediction = model(input_tensor)  # Output: future SDO imagery
-
-# Post-process prediction (denormalize, visualize, etc.)
-print(prediction.shape)  # Expected: similar shape to input, shifted in time
-
-# For fine-tuning with LoRA on downstream tasks (e.g., solar flare forecasting)
-# Use libraries like peft (Parameter-Efficient Fine-Tuning)
-from peft import LoraConfig, get_peft_model
-
-lora_config = LoraConfig(
-    r=16,  # Rank
-    lora_alpha=32,
-    target_modules=["attention"],  # Target long-short attention modules
-    lora_dropout=0.05
-)
-model = get_peft_model(model, lora_config)
-
-# Then train on your dataset
-# optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
-# ... training loop ... 
-
-# Refer to downstream_examples in the repo for specific tasks like finetune.py for solar flare forecasting
-# Example: cd downstream_examples/solar_flare_forecasting; torchrun --nproc_per_node=1 finetune.py
+from surya.model import Surya
+import numpy as np
+from PIL import Image
+import warnings
+
+# Suppress warnings for a cleaner demo experience
+warnings.filterwarnings("ignore")
+
+# --- Model Loading ---
+@gr.cache
+def load_model():
+    """
+    Downloads the pre-trained Surya model weights and initializes the model.
+    This function is cached so the model is only loaded once.
+    """
+    checkpoint_path = hf_hub_download(
+        repo_id="nasa-ibm-ai4science/Surya-1.0",
+        filename="surya.366m.v1.pt"
+    )
+
+    model = Surya(
+        img_size=4096,
+        patch_size=16,
+        in_chans=13,
+        embed_dim=1280,
+        spectral_blocks=2,
+        attention_blocks=8,
+    )
+
+    model.load_state_dict(torch.load(checkpoint_path, map_location="cpu"))
+    model.eval()
+    return model
+
+model = load_model()
+
+# --- Core Prediction Logic ---
+def predict_solar_activity(time_steps, forecast_horizon):
+    """
+    Generates a forecast of solar activity using the Surya model.
+    For this demo, we use a dummy input tensor to simulate the model's input.
+    In a real-world scenario, this function would fetch and preprocess
+    actual SDO data for the given time steps.
+    """
+    # Create a dummy input tensor representing a sequence of solar observations
+    # Shape: [batch_size, channels, time_steps, height, width]
+    dummy_input = torch.randn(1, 13, time_steps, 4096, 4096)
+
+    # In a real application, you would replace the dummy input with actual,
+    # preprocessed data from the Solar Dynamics Observatory (SDO).
+    # Preprocessing would involve alignment and normalization as described
+    # in the Surya paper.
+
+    with torch.no_grad():
+        # The model's prediction would be based on the forecast_horizon
+        # For this demo, we simulate a prediction by selecting a slice of the input
+        prediction = model(dummy_input)
+
+    # --- Visualization ---
+    # For demonstration, we will visualize one of the output channels.
+    # We will take the last predicted time step.
+    predicted_image_tensor = prediction[0, 0, -1, :, :] # Visualizing the first channel
+
+    # Normalize the tensor to a 0-255 range for image display
+    normalized_tensor = (predicted_image_tensor - predicted_image_tensor.min()) / \
+                        (predicted_image_tensor.max() - predicted_image_tensor.min())
+    image_array = (normalized_tensor * 255).byte().cpu().numpy()
+    predicted_image = Image.fromarray(image_array)
+
+    # For the flare prediction, we'll generate a dummy probability
+    flare_probability = np.random.rand()
+    if flare_probability > 0.5:
+        flare_class = "M-class or X-class Flare"
+        confidence = flare_probability
+    else:
+        flare_class = "No significant flare"
+        confidence = 1 - flare_probability
+
+    return predicted_image, {flare_class: confidence}
+
+# --- Gradio Interface Definition ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        <div align="center">
+        # ☀️ Surya: Foundation Model for Heliophysics ☀️
+        *A Gradio Demo for NASA's Solar Foundation Model*
+        </div>
+        """
+    )
+    gr.Markdown(
+        "Surya is a 366M-parameter foundation model trained on full-resolution, multi-instrument SDO observations. "
+        "This demo showcases its capability to forecast solar dynamics."
+    )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### ⚙️ Prediction Parameters")
+            time_steps_slider = gr.Slider(
+                minimum=1, maximum=10, value=5, step=1,
+                label="Number of Input Time Steps (12-min cadence)",
+                info="Represents the sequence of past solar observations to feed the model."
+            )
+            forecast_horizon_slider = gr.Slider(
+                minimum=1, maximum=24, value=1,
+                label="Forecast Horizon (hours)",
+                info="How far into the future to predict."
+            )
+            predict_button = gr.Button("🔮 Generate Forecast", variant="primary")
+
+        with gr.Column(scale=2):
+            gr.Markdown("### 🛰️ Predicted Solar Image")
+            output_image = gr.Image(label="Forecasted SDO Image (AIA 171 Å)", height=512, width=512)
+            gr.Markdown("### 💥 Solar Flare Prediction")
+            output_flare = gr.Label(label="Flare Probability")
+
+    predict_button.click(
+        fn=predict_solar_activity,
+        inputs=[time_steps_slider, forecast_horizon_slider],
+        outputs=[output_image, output_flare]
+    )
+
+    gr.Markdown("---")
+    gr.Markdown(
+        "**Note:** This demo uses a placeholder for real-time data fetching and displays a simulated prediction. "
+        "The core of this application is the loaded Surya model from NASA and IBM."
+    )
+    gr.Markdown(
+        "For more information, visit the [Surya model card on Hugging Face](https://huggingface.co/nasa-ibm-ai4science/Surya-1.0)."
+    )
+
+if __name__ == "__main__":
+    demo.launch()