table fix

README.md

@@ -8,6 +8,20 @@ sdk_version: 5.5.0
 app_file: app.py
 pinned: false
 license: bsd-2-clause
+preload_from_hub:
+  -  facebook/esm-1b
+  -  facebook/esm2_t6_8M_UR50D
+  -  facebook/esm2_t33_650M_UR50D
+  -  facebook/esm2_t30_150M_UR50D
+  -  facebook/esm2_t12_35M_UR50D
+  -  facebook/esm1b_t33_650M_UR50S
+  -  facebook/esm2_t36_3B_UR50D
+  -  facebook/esm2_t48_15B_UR50D
+  -  facebook/esm1v_t33_650M_UR90S_5
+  -  facebook/esm1v_t33_650M_UR90S_4
+  -  facebook/esm1v_t33_650M_UR90S_3
+  -  facebook/esm1v_t33_650M_UR90S_2
+  -  facebook/esm1v_t33_650M_UR90S_1
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,4 +1,4 @@
-from gradio import Blocks, Button, Checkbox, DownloadButton, Dropdown, Examples, File, Image, Markdown, Textbox
+from gradio import Blocks, Button, Checkbox, DataFrame, DownloadButton, Dropdown, Examples, File, Image, Markdown, Textbox
 
 from model import get_models
 from data import Data
@@ -19,8 +19,12 @@ def app(*argv):
     # Calculate the data based on the input parameters
     data = Data(seq, trg, model_name, scoring).calculate()
 
-    # If no error occurs, return the calculated data
-    return Image(value=data.image(), type='filepath', visible=True), DownloadButton(value=data.csv(), visible=True)
+    if isinstance(data.image(), str):
+        out = Image(value=data.image(), type='filepath', visible=True), DataFrame(visible=False)
+    else:
+        out = Image(visible=False), DataFrame(value=data.image(), visible=True)
+        
+    return *out, DownloadButton(value=data.csv(), visible=True)
 
 # Create the Gradio interface
 with open("instructions.md", "r", encoding="utf-8") as md, Blocks() as esm_scan:
@@ -41,13 +45,14 @@ with open("instructions.md", "r", encoding="utf-8") as md, Blocks() as esm_scan:
     )
     model_name = Dropdown(MODELS, label="Model", value="facebook/esm2_t30_150M_UR50D")
     scoring_strategy = Checkbox(value=True, label="Use higher accuracy scoring", interactive=True)
+    btn = Button(value="Run", variant="primary")
     dlb = DownloadButton(label="Download raw data", visible=False)
     out = Image(visible=False)
-    btn = Button(value="Run", variant="primary")
+    ouu = DataFrame(visible=False)
     btn.click(
         fn=app,
         inputs=[seq, trg, model_name],
-        outputs=[out, dlb]
+        outputs=[out, ouu, dlb]
     )
     ex = Examples(
         examples=[

data.py

@@ -1,4 +1,3 @@
-import dataframe_image as dfi
 from math import ceil
 import matplotlib.pyplot as plt
 import pandas as pd
@@ -86,11 +85,11 @@ class Data:
                 self.sort_by_score()
             else:
                 raise RuntimeError(f"Unrecognised mode {self.mode}")
-            out_df = (self.out.style
-                        .format(lambda x: f'{x:.2f}' if isinstance(x, float) else x)
-                        .hide(axis=0).hide(axis=1)
-                        .background_gradient(cmap="RdYlGn", vmax=8, vmin=-8))
-            dfi.export(out_df, self.out_img, max_rows=-1, max_cols=-1, dpi=300)
+            self.out.columns = [str(i) for i in range(self.out.shape[1])]
+            self.out_img = (self.out.style
+                                .format(lambda x: f'{x:.2f}' if isinstance(x, float) else x)
+                                .hide(axis=0)
+                                .background_gradient(cmap="RdYlGn", vmax=8, vmin=-8))
             self.out.to_csv(self.out_csv, float_format='%.2f', index=False, header=False)
 
     def sort_by_score(self):

instructions.md

@@ -2,10 +2,6 @@
 
 Calculate the <u>fitness of single amino acid substitutions</u> on proteins, using a [zero-shot](https://doi.org/10.1101/2021.07.09.450648) [language model predictor](https://github.com/facebookresearch/esm)
 
-If you use this tool in your research, please cite:
-- Totaro, M.G. (2023). “ESM-Scan - a tool to guide amino acid substitutions.” bioRxiv. [doi.org/10.1101/2023.12.12.571273](https://doi.org/10.1101/2023.12.12.571273)
-- Meier, J. (2021). “Language Models Enable Zero-Shot Prediction of the Effects of Mutations on Protein Function.” bioRxiv (Cold Spring Harbor Laboratory), July. [doi.org/10.1101/2021.07.09.450648](https://doi.org/10.1101/2021.07.09.450648)
-
 <details>
   <summary> <b> USAGE INSTRUCTIONS </b> </summary>
 
@@ -45,21 +41,21 @@ If the server remains idle for a period, it will enter standby mode. Running a c
 Results are displayed in a color-coded table, except for deep mutational scans, which produce a heatmap.  
 In the table:
 
-- Beneficial substitutions are highlighted in blue with positive values.
-- Detrimental substitutions appear in red with negative values.
+- Beneficial substitutions are highlighted in green with positive values.  
+- Detrimental substitutions appear in red with negative values.  
 
 As a rule of thumb, score differences of *4* or more are considered significant. For instance:
 
 - A substitution scoring *-6* is likely detrimental to protein functionality.
 - A score of *+2* is generally regarded as neutral.
 
-You can download the output raw data from the **button at the bottom of the page.
+The **Download raw data** button lets you download the output in CSV format.  
 
-<b>
-If you use this tool in your research, please cite:
+
+**If you use this tool in your research, please cite**:  
 
 - Totaro, M.G. (2023). “ESM-Scan - a tool to guide amino acid substitutions.” bioRxiv. [doi.org/10.1101/2023.12.12.571273](https://doi.org/10.1101/2023.12.12.571273)
 - Meier, J. (2021). “Language Models Enable Zero-Shot Prediction of the Effects of Mutations on Protein Function.” bioRxiv (Cold Spring Harbor Laboratory), July. [doi.org/10.1101/2021.07.09.450648](https://doi.org/10.1101/2021.07.09.450648)
-</b>
 
 </details>
+

requirements.txt

@@ -1,4 +1,3 @@
-dataframe-image
 gradio
 pandas
 seaborn