Update app.py

app.py

@@ -12,7 +12,7 @@ You can use this Space to test out the current model [intfloat/e5-mistral-7b-ins
 You can also use 🐣e5-mistral🛌🏻 by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/e5?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
 Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord:  [![Let's build the future of AI together! 🚀🤖](https://discordapp.com/api/guilds/1109943800132010065/widget.png)](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [Poly](https://github.com/tonic-ai/poly)
 """
-# Define the function to pool the last token
+
 def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
     left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
     if left_padding:
@@ -22,56 +22,41 @@ def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tenso
         batch_size = last_hidden_states.shape[0]
         return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
 
-# Define the function to get detailed instruct
 def get_detailed_instruct(task_description: str, query: str) -> str:
     return f'Instruct: {task_description}\nQuery: {query}'
 
-# Load tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-mistral-7b-instruct')
 model = AutoModel.from_pretrained('intfloat/e5-mistral-7b-instruct')
 
 @spaces.GPU
 def compute_embeddings(*input_texts):
-    # Check if GPU is available and use it; otherwise, use CPU
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    # Move model to the chosen device
     model.to(device)
     max_length = 4096
     task = 'Given a web search query, retrieve relevant passages that answer the query'
 
-    # Prepare the input texts
     processed_texts = [get_detailed_instruct(task, text) for text in input_texts]
-
-    # Tokenize the input texts
     batch_dict = tokenizer(processed_texts, max_length=max_length - 1, return_attention_mask=False, padding=False, truncation=True)
     batch_dict['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in batch_dict['input_ids']]
     batch_dict = tokenizer.pad(batch_dict, padding=True, return_attention_mask=True, return_tensors='pt')
-
-    # Get model outputs
     outputs = model(**batch_dict)
     embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
-
-    # Normalize embeddings
     embeddings = F.normalize(embeddings, p=2, dim=1)
-    return embeddings.detach().cpu().numpy()
-
+    embeddings_list = embeddings.detach().cpu().numpy().tolist()
+    return embeddings_list
     
 def app_interface():
     with gr.Blocks() as demo:
         gr.Markdown(title)
         gr.Markdown(description)
         
-        # Input text boxes
         input_text_boxes = [gr.Textbox(label=f"Input Text {i+1}") for i in range(4)]
         
-        # Button to compute embeddings
         compute_button = gr.Button("Compute Embeddings")
         
-        # Output display
-        output_display = gr.Dataframe(headers=["Embedding"], datatype=["numpy"])
+        output_display = gr.Dataframe(headers=["Embedding Value"], datatype=["number"])
         
-        # Layout
         with gr.Row():
             with gr.Column():
                 for text_box in input_text_boxes:
@@ -80,7 +65,6 @@ def app_interface():
                 compute_button.render()
                 output_display.render()
 
-        # Function call
         compute_button.click(
             fn=compute_embeddings,
             inputs=input_text_boxes,