Integrated with new API

app.py

@@ -1,116 +1,116 @@
-import gradio as gr
-from Clustering import ClusteringData
-import numpy as np
-from PIL import Image
-import requests
-import tempfile
-import os
-import logging
-import json
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-cd = ClusteringData()
-cd.load_model_data()
-logger.info("Clustering data loaded")
-
-
-def search_images(text_query, uploaded_image, search_mode, top_k):
-    preview = None
-    results = []
-
-    if search_mode == "Text" and text_query.strip():
-        response = requests.get(
-            f"https://ashish-001-text-embedding-api.hf.space/embedding?text={text_query.strip()}")
-        if response.status_code == 200:
-            logger.info("Embedding returned successfully by text API")
-            data = json.loads(response.content)
-            embedding = data["embedding"]
-            results = cd.find_similar_records(embedding, k=top_k)
-        else:
-            logger.info(f"{response.status_code} returned by the text API")
-            results = []
-        results = [os.path.join("coco", "val2017", "val2017", fname)
-                   for i, fname in enumerate(results)]
-        return None, results
-
-    elif search_mode == "Image":
-        if uploaded_image is not None:
-            preview = uploaded_image
-            tmp_path = uploaded_image
-            # with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp_file:
-            #     uploaded_image.save(tmp_file.name)
-            #     tmp_path = tmp_file.name
-        else:
-            preview = 'Image.jpg'
-            tmp_path = 'Image.jpg'
-        url = "https://ashish-001-clip-image-embedding-api.hf.space/clip/process"
-        files = {"file": open(tmp_path, "rb")}
-        response = requests.post(url, files=files)
-        if response.status_code == 200:
-            embedding = np.array(response.json()['embedding']).squeeze()
-            logger.info("Embedding returned successfully by image API")
-            results = cd.find_similar_records(embedding, k=top_k)
-        else:
-            logger.info(
-                f"{response.status_code} returned by the image API")
-            results = []
-        results = [os.path.join("coco", "val2017", "val2017", fname)
-                   for i, fname in enumerate(results)]
-
-        return preview, results
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("## Multimodal Image Search with CLIP")
-    gr.Markdown("Search images using **text** or **image upload**.")
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            # Inputs
-            search_mode = gr.Radio(
-                ["Text", "Image"], label="Search Mode", value="Text")
-            text_input = gr.Textbox(
-                label="Enter text query", placeholder="Type something...", visible=True, value='Empty street')
-            file_input = gr.Image(
-                type="filepath",
-                label="Upload image",
-                value="Image.jpg",
-                visible=False
-            )
-            top_k = gr.Slider(1, 20, value=6, step=1,
-                              label="Number of results")
-            submit_btn = gr.Button("Search")
-
-        with gr.Column(scale=2):
-            preview_img = gr.Image(label="Uploaded / Default Image")
-            result_gallery = gr.Gallery(
-                label="Results", columns=3, height="auto")
-
-    def toggle_inputs(mode):
-        if mode == "Text":
-            return (
-                gr.update(visible=True),
-                gr.update(visible=False, value=None),
-                [],
-                None
-            )
-        else:
-            return (
-                gr.update(visible=False),
-                gr.update(visible=True, value=None),
-                [],
-                "Image.jpg"
-            )
-
-    search_mode.change(toggle_inputs, inputs=search_mode,
-                       outputs=[text_input, file_input, result_gallery, preview_img])
-
-    submit_btn.click(fn=search_images,
-                     inputs=[text_input,
-                             file_input, search_mode, top_k],
-                     outputs=[preview_img, result_gallery,])
-
-demo.launch()
+import gradio as gr
+from Clustering import ClusteringData
+import numpy as np
+from PIL import Image
+import requests
+import tempfile
+import os
+import logging
+import json
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+cd = ClusteringData()
+cd.load_model_data()
+logger.info("Clustering data loaded")
+
+
+def search_images(text_query, uploaded_image, search_mode, top_k):
+    preview = None
+    results = []
+
+    if search_mode == "Text" and text_query.strip():
+        response = requests.get(
+            f"https://ashish-001-clip-api.hf.space/embedding?text={text_query.strip()}")
+        if response.status_code == 200:
+            logger.info("Embedding returned successfully by text API")
+            data = json.loads(response.content)
+            embedding = data["embedding"]
+            results = cd.find_similar_records(embedding, k=top_k)
+        else:
+            logger.info(f"{response.status_code} returned by the text API")
+            results = []
+        results = [os.path.join("coco", "val2017", "val2017", fname)
+                   for i, fname in enumerate(results)]
+        return None, results
+
+    elif search_mode == "Image":
+        if uploaded_image is not None:
+            preview = uploaded_image
+            tmp_path = uploaded_image
+            # with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp_file:
+            #     uploaded_image.save(tmp_file.name)
+            #     tmp_path = tmp_file.name
+        else:
+            preview = 'Image.jpg'
+            tmp_path = 'Image.jpg'
+        url = "https://ashish-001-clip-api.hf.space/clip/process"
+        files = {"file": open(tmp_path, "rb")}
+        response = requests.post(url, files=files)
+        if response.status_code == 200:
+            embedding = np.array(response.json()['embedding']).squeeze()
+            logger.info("Embedding returned successfully by image API")
+            results = cd.find_similar_records(embedding, k=top_k)
+        else:
+            logger.info(
+                f"{response.status_code} returned by the image API")
+            results = []
+        results = [os.path.join("coco", "val2017", "val2017", fname)
+                   for i, fname in enumerate(results)]
+
+        return preview, results
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Multimodal Image Search with CLIP")
+    gr.Markdown("Search images using **text** or **image upload**.")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Inputs
+            search_mode = gr.Radio(
+                ["Text", "Image"], label="Search Mode", value="Text")
+            text_input = gr.Textbox(
+                label="Enter text query", placeholder="Type something...", visible=True, value='Empty street')
+            file_input = gr.Image(
+                type="filepath",
+                label="Upload image",
+                value="Image.jpg",
+                visible=False
+            )
+            top_k = gr.Slider(1, 20, value=6, step=1,
+                              label="Number of results")
+            submit_btn = gr.Button("Search")
+
+        with gr.Column(scale=2):
+            preview_img = gr.Image(label="Uploaded / Default Image")
+            result_gallery = gr.Gallery(
+                label="Results", columns=3, height="auto")
+
+    def toggle_inputs(mode):
+        if mode == "Text":
+            return (
+                gr.update(visible=True),
+                gr.update(visible=False, value=None),
+                [],
+                None
+            )
+        else:
+            return (
+                gr.update(visible=False),
+                gr.update(visible=True, value=None),
+                [],
+                "Image.jpg"
+            )
+
+    search_mode.change(toggle_inputs, inputs=search_mode,
+                       outputs=[text_input, file_input, result_gallery, preview_img])
+
+    submit_btn.click(fn=search_images,
+                     inputs=[text_input,
+                             file_input, search_mode, top_k],
+                     outputs=[preview_img, result_gallery,])
+
+demo.launch()