Added inference pipeline

pipeline.py

@@ -0,0 +1,31 @@
+
+import io
+import base64
+import shutil
+from transformers import CLIPProcessor, CLIPModel, CLIPTokenizer
+
+
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        """
+        Initialize model
+        """
+        self.model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+        self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+        self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
+
+    def __call__(self, inputs: str):
+        """
+        Args:
+            inputs (:obj:`str`):
+                a string containing some text
+        Return:
+            A :obj:`list`list of floats: The features computed by the model.
+        """
+        inputs = self.tokenizer(inputs, padding=True, return_tensors="pt")
+  
+        # Compute text embeddings
+        with torch.no_grad():
+            text_features = self.model.get_text_features(**inputs)
+        return text_features[0].tolist()