Create app.py

app.py

@@ -0,0 +1,68 @@
+import streamlit as st
+from image_search import load_model, process_image, process_text, search_images
+
+st.set_page_config(
+        page_title="Bangla CLIP Search",
+        page_icon="chart_with_upwards_trend"
+    )
+st.markdown(
+    """
+<style>
+#introduction {
+    padding: 10px 20px 10px 20px;
+    background-color: #aad9fe;
+    border-radius: 10px;
+
+}
+
+#introduction p {
+    font-size: 1.1rem;
+    color: #050e14;
+
+}
+
+img {
+    padding: 5px;
+}
+</style>
+
+
+""",
+    unsafe_allow_html=True,
+)
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+
+
+st.markdown("# বাংলা CLIP সার্চ ইঞ্জিন ")
+st.markdown("""---""")
+st.markdown(
+    """
+<div id="introduction">
+
+<p>
+Contrastive Language-Image Pre-training (CLIP), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning from natural language supervision. , CLIP jointly trains an image encoder and a text encoder to predict the correct pairings of a batch of (image, text) training examples. At test time the learned text encoder synthesizes a zero-shot linear classifier by embedding the names or descriptions of the target dataset’s classes. 
+
+The model consists of an EfficientNet image encoder and a BERT encoder and was trained on multiple datasets from Bangla image-text domain.
+
+</p>
+</div>
+""",
+    unsafe_allow_html=True,
+)
+st.markdown("""---""") 
+text_query = st.text_input(":mag_right: Search Images / ছবি খুজুন", "সুন্দরবনের নদীর পাশে একটি বাঘ")
+st.markdown("""---""") 
+number_of_results = st.slider("Number of results ", 1, 100, 10)
+st.markdown("""---""") 
+
+ret_imgs, ret_scores, _, _ = search_images(text_query, "demo_images/", k = number_of_results)
+
+st.markdown("<div style='align: center; display: flex'>", unsafe_allow_html=True)
+st.image([str(result) for result in ret_imgs], caption = ["Score: " + str(r_s) for r_s in ret_scores], width=230)
+st.markdown("</div>", unsafe_allow_html=True)