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Update app.py
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app.py
CHANGED
@@ -51,7 +51,7 @@ class EmbeddingModel:
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self.model = AutoModel.from_pretrained('intfloat/e5-mistral-7b-instruct', torch_dtype=torch.float16, device_map=device)
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@spaces.GPU
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def compute_embeddings(selected_task, input_text
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max_length = 2042
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task_description = tasks[selected_task]
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processed_texts = [f'Instruct: {task_description}\nQuery: {input_text}']
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@@ -69,17 +69,22 @@ class EmbeddingModel:
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@spaces.GPU
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def compute_similarity(self, sentence1, sentence2, extra_sentence1, extra_sentence2):
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# Compute cosine similarity
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similarity1 = F.cosine_similarity(
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similarity2 = F.cosine_similarity(
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return similarity1, similarity2
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@@ -92,20 +97,19 @@ def app_interface():
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with gr.Tab("Embedding Generation"):
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input_text_box = gr.Textbox(label="📖Input Text")
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system_prompt_box = gr.Textbox(label="🤖System Prompt (Optional)")
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compute_button = gr.Button("Try🐣🛌🏻e5")
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output_display = gr.Textbox(label="🐣e5-mistral🛌🏻 Embeddings")
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compute_button.click(
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fn=EmbeddingModel.compute_embeddings,
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inputs=[task_dropdown, input_text_box
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outputs=output_display
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)
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with gr.Tab("Sentence Similarity"):
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sentence1_box = gr.Textbox(label="Sentence
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sentence2_box = gr.Textbox(label="Sentence
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extra_sentence1_box = gr.Textbox(label="Sentence
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extra_sentence2_box = gr.Textbox(label="Sentence
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similarity_button = gr.Button("Compute Similarity")
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similarity_output = gr.Label(label="🐣e5-mistral🛌🏻 Similarity Scores")
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similarity_button.click(
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@@ -116,7 +120,6 @@ def app_interface():
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with gr.Row():
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with gr.Column():
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system_prompt_box
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input_text_box
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with gr.Column():
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compute_button
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self.model = AutoModel.from_pretrained('intfloat/e5-mistral-7b-instruct', torch_dtype=torch.float16, device_map=device)
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@spaces.GPU
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def compute_embeddings(selected_task, input_text):
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max_length = 2042
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task_description = tasks[selected_task]
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processed_texts = [f'Instruct: {task_description}\nQuery: {input_text}']
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@spaces.GPU
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def compute_similarity(self, sentence1, sentence2, extra_sentence1, extra_sentence2):
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# Compute embeddings for each sentence
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embeddings1 = compute_embeddings(self.selected_task, sentence1)
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embeddings2 = compute_embeddings(self.selected_task, sentence2)
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embeddings3 = compute_embeddings(self.selected_task, extra_sentence1)
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embeddings4 = compute_embeddings(self.selected_task, extra_sentence2)
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# Convert embeddings to tensors
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embeddings_tensor1 = torch.tensor(embeddings1).to(device)
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embeddings_tensor2 = torch.tensor(embeddings2).to(device)
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embeddings_tensor3 = torch.tensor(embeddings3).to(device)
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embeddings_tensor4 = torch.tensor(embeddings4).to(device)
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# Compute cosine similarity
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similarity1 = F.cosine_similarity(embeddings_tensor1, embeddings_tensor2).item()
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similarity2 = F.cosine_similarity(embeddings_tensor1, embeddings_tensor3).item()
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similarity3 = F.cosine_similarity(embeddings_tensor1, embeddings_tensor4).item()
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return similarity1, similarity2
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with gr.Tab("Embedding Generation"):
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input_text_box = gr.Textbox(label="📖Input Text")
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compute_button = gr.Button("Try🐣🛌🏻e5")
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output_display = gr.Textbox(label="🐣e5-mistral🛌🏻 Embeddings")
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compute_button.click(
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fn=EmbeddingModel.compute_embeddings,
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inputs=[task_dropdown, input_text_box],
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outputs=output_display
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)
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with gr.Tab("Sentence Similarity"):
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sentence1_box = gr.Textbox(label="'Focus Sentence' - The 'Subject'")
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sentence2_box = gr.Textbox(label="'Input Sentence' - 1")
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extra_sentence1_box = gr.Textbox(label="'Input Sentence' - 2")
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extra_sentence2_box = gr.Textbox(label="'Input Sentence' - 3")
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similarity_button = gr.Button("Compute Similarity")
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similarity_output = gr.Label(label="🐣e5-mistral🛌🏻 Similarity Scores")
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similarity_button.click(
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with gr.Row():
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with gr.Column():
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input_text_box
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with gr.Column():
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compute_button
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