working

app.py

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+import streamlit as st
+import degirum as dg
+from PIL import Image
+import torch
+import numpy as np
+import torch.nn.functional as F
+import clip
+import cv2
+
+# Compute the cosine similarity between two vectors.
+def cosine_similarity(a, b):
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+
+def compute_text_embeddings(text_prompts):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    clip_model, _ = clip.load("RN50", device=device)
+    
+    text_embeddings = []
+    for text_prompt in text_prompts:
+        text = clip.tokenize(text_prompt).to(device)
+        text_embedding = clip_model.encode_text(text)
+        text_embeddings.append(text_embedding.cpu().detach().numpy().tolist())
+    
+    return text_embeddings
+
+zoo=dg.connect(dg.CLOUD,zoo_url='https://cs.degirum.com/degirum/kvk_upload_test', token="dg_ZPNjDPnAPpoZiykpFz2CnoYaAYf6ewBxwDCBo")#,token=st.secrets["DG_TOKEN"])
+
+st.title('DeGirum CLIP model Demo')
+
+with st.sidebar:
+    st.header('Specify Model Options Below')
+    prompts = st.text_area("Enter text prompts (comma-separated):", value="People Running, People Talking, People Fighting, People Laughing, People Dancing")
+    prompts = [prompt.strip() for prompt in prompts.split(',')]
+st.text('Upload an image. Then click on the submit button')
+with st.form("model_form"):
+    uploaded_file=st.file_uploader('input image')
+    submitted = st.form_submit_button("Submit")
+    embeddings = compute_text_embeddings(prompts)
+    if submitted:
+        model=zoo.load_model('clip--224x224_float_openvino_cpu_4',
+                             input_image_format = "RAW"                                    
+                            )
+        image = Image.open(uploaded_file)
+        opencv_image = np.array(image)
+        opencv_image = cv2.cvtColor(opencv_image, cv2.COLOR_RGB2BGR)
+        predictions=model(opencv_image).results[0]["data"]
+        dg_cloud_output_reshaped = predictions.reshape(-1)
+        similarities = [cosine_similarity(dg_cloud_output_reshaped, np.array(embedding).reshape(-1)) for embedding in embeddings]
+        similarities_tensor = torch.tensor(similarities, dtype=torch.float32)
+        softmax_scores = F.softmax(similarities_tensor, dim=0)   
+        max_index = torch.argmax(softmax_scores).item()
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        for index, prompt in enumerate(prompts):
+            st.write(f"{prompt} - {softmax_scores[index]*100:.2f}%")
+
+        # st.write(predictions.results)

requirements.txt

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+degirum
+altair<5
+clip
+opencv-python
+torch
+numpy