Changed to Text embeddings

app.py

@@ -1,31 +1,46 @@
-import torch
-import torch.nn.functional as F
-from transformers import AutoTokenizer, AutoModel, AutoImageProcessor
-import gradio as gr
-import spaces
-
-processor = AutoImageProcessor.from_pretrained("nomic-ai/nomic-embed-vision-v1.5")
-vision_model = AutoModel.from_pretrained("nomic-ai/nomic-embed-vision-v1.5", trust_remote_code=True)
-
-@spaces.GPU
-def ImgEmbed(image):
-    print(image);
-    inputs = processor(image, return_tensors="pt")
-
-    img_emb = vision_model(**inputs).last_hidden_state
-    img_embeddings = F.normalize(img_emb[:, 0], p=2, dim=1)
-
-    return img_embeddings[0].tolist();
-
-
-
-with gr.Blocks() as demo:      
-       img = gr.Image();
-       out = gr.Text();
-       
-       btn = gr.Button("Get Embeddings")
-       btn.click(ImgEmbed, [img], [out])
-       
-       
-if __name__ == "__main__":
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel, AutoImageProcessor
+import gradio as gr
+import spaces
+
+processor = AutoImageProcessor.from_pretrained("nomic-ai/nomic-embed-vision-v1.5")
+vision_model = AutoModel.from_pretrained("nomic-ai/nomic-embed-vision-v1.5", trust_remote_code=True)
+
+tokenizer = AutoTokenizer.from_pretrained('nomic-ai/nomic-embed-text-v1.5')
+text_model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True)
+text_model.eval()
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+        
+@spaces.GPU
+def TxtEmbed(text):
+    
+    sentences = [text]
+    encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+    
+    with torch.no_grad():
+        model_output = text_model(**encoded_input)
+    
+    text_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+    text_embeddings = F.layer_norm(text_embeddings, normalized_shape=(text_embeddings.shape[1],))
+    text_embeddings = F.normalize(text_embeddings, p=2, dim=1)
+    
+    return text_embeddings.to_list();
+
+
+
+
+with gr.Blocks() as demo:      
+       txt = gr.Text();
+       out = gr.Text();
+       
+       btn = gr.Button("Gerar embeddings")
+       btn.click(TxtEmbed, [txt], [out])
+       
+       
+if __name__ == "__main__":
     demo.launch(show_api=True)