Create app.py

app.py

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+import spaces
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+import gradio as gr
+
+title = """
+# 👋🏻Welcome to 🙋🏻‍♂️Tonic's 🐣e5-mistral🛌🏻Embeddings """
+description = """
+You can use this Space to test out the current model [intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct). e5mistral has a larger context window, a different prompting/return mechanism and generally better results than other embedding models. 
+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:
+        return last_hidden_states[:, -1]
+    else:
+        sequence_lengths = attention_mask.sum(dim=1) - 1
+        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()
+
+    
+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"])
+        
+        # Layout
+        with gr.Row():
+            with gr.Column():
+                for text_box in input_text_boxes:
+                    text_box.render()
+            with gr.Column():
+                compute_button.render()
+                output_display.render()
+
+        # Function call
+        compute_button.click(
+            fn=compute_embeddings,
+            inputs=input_text_boxes,
+            outputs=output_display
+        )
+
+    return demo
+
+# Run the Gradio app
+app_interface().launch()