Add aerosol pipeline

app.py

@@ -0,0 +1,125 @@
+"""
+app.py — Gradio Space para HuggingFace.
+Solo UI: llama via HTTP al api_server.py que corre en el droplet AMD.
+"""
+
+import os
+import base64
+import io
+import requests
+import gradio as gr
+from PIL import Image
+
+API_URL = os.environ.get("API_URL", "http://localhost:7860")
+
+VARIABLES = [
+    "Optical_Depth_Land_And_Ocean",
+    "Image_Optical_Depth_Land_And_Ocean",
+    "Angstrom_Exponent_1_Ocean",
+    "Mass_Concentration_Land",
+]
+
+def run_download(concept_id, bbox_w, bbox_s, bbox_e, bbox_n,
+                 date_start, date_end, n_granules, hdf_dir):
+    try:
+        r = requests.post(f"{API_URL}/download", json={
+            "concept_id": concept_id,
+            "bbox_west":  bbox_w,  "bbox_south": bbox_s,
+            "bbox_east":  bbox_e,  "bbox_north": bbox_n,
+            "date_start": date_start, "date_end": date_end,
+            "n_granules": int(n_granules),
+            "hdf_dir":    hdf_dir,
+        }, timeout=300)
+        return r.json().get("log", "Sin respuesta")
+    except Exception as e:
+        return f"Error: {e}"
+
+def run_pipeline(hdf_dir, output_dir, plots_dir, db_url, variable):
+    try:
+        r = requests.post(f"{API_URL}/pipeline", json={
+            "hdf_dir":    hdf_dir,
+            "output_dir": output_dir,
+            "plots_dir":  plots_dir,
+            "db_url":     db_url,
+            "variable":   variable,
+        }, timeout=600)
+        data = r.json()
+        images = []
+        for img in data.get("images", []):
+            raw = base64.b64decode(img["b64"])
+            images.append(Image.open(io.BytesIO(raw)))
+        status = (
+            f"Pipeline completado\n"
+            f"Filas cargadas: {data.get('rows_loaded', 0):,}\n"
+            f"Graficas: {data.get('plots_count', 0)}"
+        )
+        return status, images
+    except Exception as e:
+        return f"Error: {e}", []
+
+def run_full(concept_id, bbox_w, bbox_s, bbox_e, bbox_n,
+             date_start, date_end, n_granules,
+             hdf_dir, output_dir, plots_dir, db_url, variable):
+    dl_log = run_download(concept_id, bbox_w, bbox_s, bbox_e, bbox_n,
+                          date_start, date_end, n_granules, hdf_dir)
+    status, images = run_pipeline(hdf_dir, output_dir, plots_dir, db_url, variable)
+    return dl_log + "\n\n" + status, images
+
+with gr.Blocks(title="AMD MI300X Aerosol Pipeline") as demo:
+    gr.Markdown("# AMD MI300X Aerosol Pipeline Demo\nDescarga datos MODIS y genera visualizaciones de aerosoles con agentes LangGraph en AMD MI300X.")
+
+    with gr.Tabs():
+        with gr.TabItem("1 Descarga"):
+            with gr.Row():
+                ci1 = gr.Textbox(label="Concept ID", value="C1443528505-LAADS")
+                ng1 = gr.Slider(1, 20, value=5, step=1, label="N granules")
+            with gr.Row():
+                bw1 = gr.Number(label="Lon W", value=-10)
+                bs1 = gr.Number(label="Lat S", value=20)
+                be1 = gr.Number(label="Lon E", value=10)
+                bn1 = gr.Number(label="Lat N", value=50)
+            with gr.Row():
+                ds1 = gr.Textbox(label="Fecha inicio", value="2003-07-04")
+                de1 = gr.Textbox(label="Fecha fin",    value="2003-07-05")
+            hd1  = gr.Textbox(label="HDF dir", value="/tmp/earthdata")
+            btn1 = gr.Button("Descargar", variant="primary")
+            out1 = gr.Textbox(label="Log", lines=10)
+            btn1.click(run_download, [ci1,bw1,bs1,be1,bn1,ds1,de1,ng1,hd1], out1)
+
+        with gr.TabItem("2 Pipeline"):
+            hd2  = gr.Textbox(label="HDF dir",   value="/tmp/earthdata")
+            od2  = gr.Textbox(label="CSV dir",   value="/tmp/aerosol_csv")
+            pd2  = gr.Textbox(label="Plots dir", value="/tmp/aerosol_plots")
+            db2  = gr.Textbox(label="DB URL",    value="", type="password")
+            va2  = gr.Dropdown(VARIABLES, label="Variable", value=VARIABLES[0])
+            btn2 = gr.Button("Ejecutar pipeline", variant="primary")
+            st2  = gr.Textbox(label="Estado", lines=5)
+            gl2  = gr.Gallery(label="Graficas", columns=3, height=500)
+            btn2.click(run_pipeline, [hd2,od2,pd2,db2,va2], [st2,gl2])
+
+        with gr.TabItem("3 Flujo completo"):
+            with gr.Row():
+                ci3 = gr.Textbox(label="Concept ID", value="C1443528505-LAADS")
+                ng3 = gr.Slider(1, 20, value=5, step=1, label="N granules")
+            with gr.Row():
+                bw3 = gr.Number(label="Lon W", value=-10)
+                bs3 = gr.Number(label="Lat S", value=20)
+                be3 = gr.Number(label="Lon E", value=10)
+                bn3 = gr.Number(label="Lat N", value=50)
+            with gr.Row():
+                ds3 = gr.Textbox(label="Fecha inicio", value="2003-07-04")
+                de3 = gr.Textbox(label="Fecha fin",    value="2003-07-05")
+            hd3  = gr.Textbox(label="HDF dir",   value="/tmp/earthdata")
+            od3  = gr.Textbox(label="CSV dir",   value="/tmp/aerosol_csv")
+            pd3  = gr.Textbox(label="Plots dir", value="/tmp/aerosol_plots")
+            db3  = gr.Textbox(label="DB URL",    value="", type="password")
+            va3  = gr.Dropdown(VARIABLES, label="Variable", value=VARIABLES[0])
+            btn3 = gr.Button("Ejecutar todo", variant="primary")
+            lg3  = gr.Textbox(label="Log", lines=12)
+            im3  = gr.Gallery(label="Graficas", columns=3, height=500)
+            btn3.click(run_full, [ci3,bw3,bs3,be3,bn3,ds3,de3,ng3,hd3,od3,pd3,db3,va3], [lg3,im3])
+
+    gr.Markdown("---\nPowered by AMD MI300X | vLLM | LangGraph | NASA EarthData | MODIS MYD04_3K")
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,14 @@
+openai>=1.0.0
+langgraph>=0.2.0
+langchain>=0.3.0
+langchain-openai>=0.2.0
+langchain-mcp-adapters>=0.1.0
+mcp>=1.0.0
+earthaccess>=0.9.0
+pyhdf>=0.11.0
+numpy>=1.26.0
+pandas>=2.2.0
+matplotlib>=3.9.0
+psycopg2-binary>=2.9.0
+python-dotenv>=1.0.0
+tqdm>=4.66.0