Create text features

app.py

@@ -14,6 +14,7 @@ TEXT_PROMPTS_FILE_NAME = "text_prompts.json"
 LOGISTIC_REGRESSION_MODEL_FILE_NAME = "logistic_regression_l1_oct_2.pkl"
 
 
+
 clip_model, preprocess = clip.load(CLIP_MODEL_NAME, device="cpu")
 
 with open(
@@ -28,15 +29,28 @@ with open(
 ) as f:
     lr_model = pickle.load(f)
 
+logger.info("Logistic regression model loaded, coefficients: ")
+
+
+all_text_features = []
+with torch.no_grad():
+    for k, prompts in text_prompts.items():
+        assert len(prompts) == 2
+        inputs = clip.tokenize(prompts)
+        outputs = clip_model.encode_text(inputs)
+        all_text_features.append(outputs)
+    all_text_features = torch.cat(all_text_features, dim=0)
+    all_text_features = all_text_features.cpu().numpy()
+
 
-def greet(name):
+def predict_fn(name):
     return "Hello " + name + "!"
 
 
 iface = gr.Interface(
-    fn=greet,
+    fn=predict_fn,
     inputs="image",
     outputs="text",
     allow_flagging="manual"
 )
-iface.launch()
+iface.launch()

requirements.txt

@@ -4,3 +4,5 @@ ftfy
 regex
 tqdm
 git+https://github.com/openai/CLIP.git
+scikit-learn
+scipy