Create Gradio Interface

Signed-off-by: Snehil Shah <snehilshah.989@gmail.com>

app.py

@@ -0,0 +1,58 @@
+import gradio as gr
+from qdrant_client import QdrantClient
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("clip-ViT-B-32")
+
+qdrant_client = QdrantClient(
+    url = 'url',
+    port= 443,
+    api_key = "key",
+)
+
+def search_images(modality, count, input_text, input_image):
+    query = str(input_text) if modality=='Text' else input_image
+
+    results = qdrant_client.search(
+    collection_name = "images",
+    query_vector = model.encode(query).tolist(),
+    with_payload = True,
+    limit = count
+    )
+
+    return [gr.update(value="## Results")]+[gr.update(value=result.payload['url'], visible=True) for result in results]+[gr.update(visible=False)]*(100-count)
+
+def input_interface(choice):
+    if choice == "Text":
+        return [gr.update(visible=True), gr.update(visible=False)]
+    else:
+        return [gr.update(visible=False), gr.update(visible=True)]
+
+with gr.Blocks() as interface:
+    gr.Markdown("# Multi-Modal Image Search Engine\nSemantically search over 15k images using text or image inputs. The image data is limited, don't expect to find everything!")
+
+    # Input Interface
+    with gr.Column(variant='compact'):
+        input_type = gr.Radio(choices=["Text", "Image"], type="value", label="Modality", value="Text")
+        with gr.Column() as text_area:
+            text_input = gr.Textbox(label="Text", lines=1, placeholder="Try 'Golden Retriever'")
+        with gr.Column(visible=False) as image_uploader:
+            image_input = gr.Image(type="pil")
+    input_type.change(input_interface, input_type, [text_area, image_uploader])
+
+    # Search Controls
+    with gr.Column(variant="panel"):
+        count = gr.Slider(minimum=1, maximum=40, step=1, value=8, label="No. of Results")
+        images_btn = gr.Button(value="Search Images", variant="primary")
+
+    # Output Interface
+    images = []
+    images.append(gr.Markdown())
+    with gr.Column() as output_images:
+        for i in range(10):
+            with gr.Row():
+                for j in range(4):
+                    images.append(gr.Image(visible=False))
+    images_btn.click(search_images, inputs=[input_type, count, text_input, image_input], outputs=images)
+
+    interface.launch()

models.txt

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-torchvision
-resnet
-OpenAI CLIP

requirements.txt

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+gradio==4.13.0
+qdrant-client==1.7.0
+sentence-transformers==2.2.2