update documentation

app.py

@@ -64,14 +64,45 @@ description = """You provide a sentence and our few-shot fine tuned CLIP model w
 """
 article = """
 \n
-+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 \n
-We fine tuned Open Ai's CLIP model on both text (tweets) and images of emoji's!\n
-The current model is fine-tuned on 15 samples per emoji.
+I fine tuned Open Ai's CLIP model on both text (tweets) and images of emoji's!\n
+The current model you can play with is fine-tuned on 15 samples per emoji.
 
 - model: https://huggingface.co/vincentclaes/emoji-predictor \n
 - dataset: https://huggingface.co/datasets/vincentclaes/emoji-predictor \n
 - profile: https://huggingface.co/vincentclaes \n
+
+Below you can find a table with the precision for predictions and suggestions 
+for a range of samples per emoji we fine-tuned CLIP on.
+
+The column "Prediction" indicates the precision for predicting the right emoji.
+
+Since there can be some confusion about the right emoji for a tweet, 
+I also tried to present 4 suggestions. If 1 of the 4 suggestions is the same as the label, 
+I consider it a valid prediction. See the column "Suggestion".
+
+Randomly predicting an emoji would have a precision of 1/32 or 0.0325.
+Randomly suggesting an emoji would have a precision of 4/32 or 0.12.
+
+
+           | Samples  | Prediction  | Suggestion  |
+           |--------- |------------ |------------ |
+           |    0     |    0.13     |    0.33     |
+           |    1     |    0.11     |    0.30     |
+           |    5     |    0.14     |    0.38     |
+           |    10    |    0.20     |    0.45     |
+           |    15    |    0.22     |    0.51     |
+           |    20    |    0.19     |    0.49     |
+           |    25    |    0.24     |    0.54     |
+           |    50    |    0.23     |    0.53     |
+           |   100    |    0.25     |    0.57     |
+           |   250    |    0.29     |    0.62     |
+           |   500    |    0.29     |    0.63     |
+           
+           
+
+
 """
 examples = [
     "I'm so happy for you!",