first diry commit for checking dependencies.

app.py

@@ -1,154 +1,277 @@
 import gradio as gr
+from astropy.io import fits
+import matplotlib.pyplot as plt
 import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+import io
+from PIL import Image
+import astropy.units as u
+from astropy.wcs import WCS
+from astropy.coordinates import SkyCoord
+from astropy import coordinates as coord
+from astropy.wcs.utils import skycoord_to_pixel
+from astroquery.simbad import Simbad
+import pandas as pd
+import matplotlib.patches as patches
+# Increase the limit (set to a value larger than the pixel count of your image)
+Image.MAX_IMAGE_PIXELS = None
+plt.style.use('dark_background')
+
+# Initialize globals
+global_dataframe = pd.DataFrame()
+global_data = None
+global_header = None
+
+def show_csv(file):
+    """
+    Displays the uploaded CSV file as a table.
+    """
+    global global_dataframe
+    
+    try:
+        # Read the CSV file into a pandas DataFrame
+        df = pd.read_csv(file.name, index_col=0)
+        global_dataframe = df  # Store the dataframe globally for filtering
+        # Extract unique types from the "type" column
+        if "TYPE" in df.columns:
+            unique_types = df["TYPE"].unique().tolist()
+            return df, gr.CheckboxGroup(label="Select Catalogue", choices=unique_types, value=unique_types, interactive=True)
+        else:
+            return "Error: CSV does not contain a 'type' column.", None
+    except Exception as e:
+        return f"Error: {str(e)}", None
+    
+# Define a function to be called when the button is clicked
+def query_update_table():
+    """
+    Displays the uploaded CSV file as a table.
+    """
+    global global_dataframe, global_header, global_data
+    
+    try:
+        # Read the CSV file into a pandas DataFrame
+        #df = pd.read_csv('dataframe.csv', index_col=0)
+
+        Simbad.TIMEOUT = 120
+
+        # Define the specific coordinates
+        wcs = WCS(global_header).dropaxis(2)
+        center_ra = global_header['CRVAL1']
+        center_dec = global_header['CRVAL2']
+        target_coord = SkyCoord(ra=center_ra, dec=center_dec, unit=(u.deg, u.deg), frame='icrs')
+        print(center_ra, center_dec)
+        # define the search radius
+        radius_deg = max([abs(global_header['CDELT1']),abs(global_header['CDELT2'])])*max([global_header['NAXIS1'],global_header['NAXIS2']])
+        radius_deg *= 1
+        # Set up the query criteria
+        if target_coord.dec.deg > 0:
+            custom_query = f"region(CIRCLE, {target_coord.ra.deg} +{target_coord.dec.deg}, {radius_deg}d)"
+        else:
+            custom_query = f"region(CIRCLE, {target_coord.ra.deg} {target_coord.dec.deg}, {radius_deg}d)"
+        
+        print(f'Query={custom_query}')
+
+        result_table = Simbad.query_criteria(custom_query, otype='galaxy')
+        
+        print("received feedback from simbad!!!")
+        print(result_table)
+        df = result_table.to_pandas().set_index('main_id')
+        print(df.columns)
+        df['Pixel_Position'] = [skycoord_to_pixel(SkyCoord(v[0],v[1], unit=(u.deg, u.deg), frame='icrs'), wcs) for v in df[['ra','dec']].values]
+        print(df['Pixel_Position'])
+        df['px'] = df['Pixel_Position'].apply(lambda x: int(x[0]))
+        df['py'] = df['Pixel_Position'].apply(lambda x: int(x[1]))
+        mask = (df.px>0)&(df.px< global_data.shape[1])&(df.py>0)&(df.py<global_data.shape[0])
+        print(df)
+        df = df[mask]
+        df = df.reset_index()
+        df['TYPE'] = df['main_id'].apply(lambda x: x.split(' ')[0].split('+')[0])
+        df = df.sort_values(by=['px', 'py'], ascending=[True, True]).reset_index(drop=True)
+        print(df)
+        df = df.iloc[:200]
+        global_dataframe = df  # Store the dataframe globally for filtering
+
+        # Extract unique types from the "type" column
+        if "TYPE" in df.columns:
+            unique_types = df["TYPE"].unique().tolist()
+            return df, gr.CheckboxGroup(label="Select Catalogue", choices=unique_types, value=unique_types, interactive=True)
+        else:
+            return "Error: CSV does not contain a 'type' column.", None
+    except Exception as e:
+        return f"Error: {str(e)}", None
+
+
+def load_fits_image(file, type_checkboxes, title, axis_options, num_rows, patch_size, fontsize, alpha, linewidth, scale, patch_color, sort_method):
+    """
+    Displays the data from the uploaded FITS file.
+    """
+    global global_header, global_data
+
+    # Open the FITS file
+    hdu = fits.open(file)
+    data = hdu[0].data  # Access the primary HDU data
+    data = np.swapaxes(np.swapaxes(data,0,2),0,1)#.astype(np.float)
+    #data = (data*255).astype(np.uint8)  # Access the primary HDU data
+    global_data = data
+
+    # get fits header
+    header = hdu[0].header
+    global_header = header
+ #selected_types, title, selected_axis_options, num_rows, patch_size, patch_color, sort_method
+    return update_images_and_tables(type_checkboxes, title, axis_options, num_rows, patch_size, fontsize, alpha, linewidth, scale, patch_color, sort_method)
+ 
+def update_images_and_tables(selected_types, title, selected_axis_options, num_rows, patch_size, fontsize, alpha, linewidth, scale, patch_color, sort_method):
+    global global_dataframe, global_header, global_data
+    
+    if selected_types and not global_dataframe.empty:
+        # Filter the dataframe based on the selected types
+        filtered_df = global_dataframe[global_dataframe["TYPE"].isin(selected_types)]
+        mask = (filtered_df.px-patch_size//2 > 0)&(filtered_df.px+patch_size//2 < global_data.shape[1])&(filtered_df.py-patch_size//2 > 0)&(filtered_df.py+patch_size//2 < global_data.shape[0])
+        filtered_df = filtered_df[mask]
+    else:
+        filtered_df = None
+
+    if not filtered_df is None:
+        # Sort the dataframe based on the sorting method
+        if sort_method == "by Catalogue":
+            filtered_df = filtered_df.sort_values(by=['px', 'py'], ascending=[True, True])
+            filtered_df = filtered_df.sort_values(by='TYPE', ascending=True).reset_index(drop=True)
+        elif sort_method == "by x":
+            filtered_df = filtered_df.sort_values(by=['px', 'py'], ascending=[True, True]).reset_index(drop=True)
+        elif sort_method == "by y":
+            filtered_df = filtered_df.sort_values(by=['py', 'px'], ascending=[True, True]).reset_index(drop=True)
+
+    try:
+        
+        wcs = WCS(global_header).dropaxis(2)
+        
+        ratio = global_data.shape[0]/global_data.shape[1]
+        # Plot WCS
+        fig = plt.figure(figsize=(ratio*scale,scale))
+        ax = fig.add_subplot(projection=wcs, label='overlays')
+        ax.imshow(global_data, origin='lower')
+
+        #if not filtered_df is None:
+        #    filtered_df.plot.scatter(x='px', y='py', ax=ax, s=15, c=patch_color)
+
+        if "with Grid" in selected_axis_options:
+            ax.coords.grid(True, color='white', ls='-', alpha=.5)
+        if "with Axis Annotation" in selected_axis_options:
+            ax.coords[0].set_axislabel('Right Ascension (J2000)', fontsize=fontsize+2)
+            ax.coords[1].set_axislabel('Declination (J2000)', fontsize=fontsize+2)
+        else:
+            ax.axis('off')
+        plt.title(title, fontsize=fontsize+4)
+
+        if not filtered_df is None:
+            all_patches = []
+            for i,row in filtered_df.iterrows():
+                rect = patches.Rectangle((row.px-patch_size//2, row.py-patch_size//2), patch_size, patch_size, alpha=alpha, linewidth=linewidth, edgecolor=patch_color, facecolor='none')
+                ax.add_patch(rect)
+                ax.text(row.px,row.py+patch_size//2,str(i+1),
+                        ha='center',va='bottom',color=patch_color,fontsize=fontsize)
+                patch = global_data[row.py-patch_size//2:row.py+patch_size//2,row.px-patch_size//2:row.px+patch_size//2]
+                all_patches.append(patch)
+        plt.tight_layout()
+        # Convert the plot to an image
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=.1, dpi=200)
+        plt.close(fig)
+        buf.seek(0)
+        # Convert buffer to PIL Image
+        image = Image.open(buf)
+
+        if not filtered_df is None:
+            
+            m = num_rows
+            n = int(np.ceil(len(filtered_df)/m))
+
+            second_scale=max([1,scale//3])
+            fig, axarr = plt.subplots(n,m,figsize=(m*second_scale,n*second_scale))
+            for i, row in filtered_df.iterrows():
+                    ax = axarr[i//m,i%m]
+                    ax.imshow(all_patches[i][::-1])
+                    ax.set_title(row.main_id, fontsize=fontsize-2)
+                    ax.set_xticks([])
+                    ax.set_yticks([])
+                    ax.text(2,2,str(i+1)[:30],ha='left',va='top',fontsize=fontsize+6)
+            for i in np.arange(len(all_patches),m*n):
+                    ax = axarr[i//m,i%m]
+                    ax.axis('off')
+            plt.tight_layout()
+
+            # Convert the plot to an image
+            second_buf = io.BytesIO()
+            plt.savefig(second_buf, format='png', bbox_inches='tight', pad_inches=.1, dpi=200)
+            plt.close(fig)
+            second_buf.seek(0)
+            # Convert buffer to PIL Image
+            patches_image = Image.open(second_buf)
+
+            return filtered_df, image, patches_image
+        else:
+            return filtered_df, image, None
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+# Gradio interface
+with gr.Blocks(css=".btn-green {background-color: green; color: white;}") as gui:
+    gr.Markdown("# What's in my image?")
+    # Options Area
+    with gr.Row() as options_gui:
+        num_rows = gr.Number(label="Number of Rows", value=16, minimum=2, precision=0, interactive=True)
+        title = gr.Textbox(label="Image Title", value="Custom Title", interactive=True)
+        patch_size = gr.Slider(label="Patch Size", minimum=16, maximum=128, step=8, value=32, 
+        interactive=True)
+        fontsize = gr.Slider(label="Fontsize", minimum=6, maximum=26, step=1, value=10, 
+        interactive=True)
+        alpha = gr.Slider(label="Alpha", minimum=0., maximum=1., step=.1, value=1., 
+        interactive=True)
+        linewidth = gr.Slider(label="Linewidth", minimum=1, maximum=4, step=1, value=1, 
+        interactive=True)
+        scale = gr.Slider(label="Scale", minimum=1, maximum=20, step=1, value=8, 
+        interactive=True)
+
+        patch_color = gr.ColorPicker(label="Patch Color", value="#FFFFFF", interactive=True)
+        sort_method = gr.Dropdown(label="Sorting Method", choices=["by Catalogue", "by x", "by y"], value="by Catalogue", interactive=True)
+        axis_options = gr.CheckboxGroup(
+            label="Select options",
+            choices=["with Grid", "with Axis Annotation"],
+            value=["with Grid", "with Axis Annotation"],  # Preselected values
+            interactive=True  # Makes it interactive
+        )
+    gr.Markdown("Upload a plate solved `.fits` file (32 bit) to display its content.")
+    
+    file_input = gr.File(label="Upload .fits File", type="filepath")
+    #file_input_csv = gr.File(label="Upload .csv File")
+    greet_button = gr.Button("Query Simbad for Galaxies")  # Create the button
+    fits_image = gr.Image(label="Input Image", type="pil")
+    type_checkboxes = gr.CheckboxGroup(label="Select Catalogue")
+    patches_image = gr.Image(label="Patches Image", type="pil")
+    csv_table = gr.DataFrame(label="CSV Table")
+
+    track_options = [type_checkboxes, title, axis_options, num_rows, patch_size, fontsize, alpha, linewidth, scale, patch_color, sort_method]
+
+    file_input.change(load_fits_image, 
+                      inputs=[file_input] + track_options, 
+                      outputs=[csv_table,fits_image,patches_image])
+        
+    for option_i in track_options:
+        option_i.change(update_images_and_tables,
+                       inputs=track_options, 
+                      outputs=[csv_table,fits_image,patches_image])
+
+    # Display CSV table
+    #file_input_csv.change(show_csv, 
+    #                      inputs=file_input_csv, 
+    #                      outputs=[csv_table, type_checkboxes])
+        
+    greet_button.click(query_update_table, inputs=None, outputs=[csv_table, type_checkboxes])
+
+    # Update the selected checkboxes change
+    type_checkboxes.change(update_images_and_tables, 
+                           inputs=track_options,
+                           outputs=[csv_table,fits_image,patches_image])
+
+gui.launch(debug=True)

requirements.txt

@@ -1,6 +1,4 @@
-accelerate
-diffusers
 invisible_watermark
-torch
-transformers
-xformers
+astropy
+astroquery
+matplotlib