Upload app.py

app.py

@@ -5,29 +5,35 @@ import streamlit as st
 import numpy as np
 from PIL import Image
 import os
-
-st.sidebar.write("# FashionCLIP Resources")
-st.sidebar.write("We have several resources related to FashionCLIP.")
-st.sidebar.write("## Documentation")
-st.sidebar.write("* 📚 [Blog Post](https://towardsdatascience.com/teaching-clip-some-fashion-3005ac3fdcc3)")
-st.sidebar.write("* 📚 [Paper](https://www.nature.com/articles/s41598-022-23052-9)")
-
-st.sidebar.write("## Code")
-st.sidebar.write("* 📚 [Repo](https://github.com/patrickjohncyh/fashion-clip)")
-st.sidebar.write("* 📚 [Colab](https://colab.research.google.com/drive/1Z1hAxBnWjF76bEi9KQ6CMBBEmI_FVDrW#scrollTo=FzUQGwS1lhGS)")
-st.sidebar.write("* 📚 [HuggingFace Weights](https://huggingface.co/patrickjohncyh/fashion-clip)")
-
-
-st.write("# FashionCLIP. A Foundation Model for Fashion.")
-
-st.write("[![Youtube Video](https://img.shields.io/badge/youtube-video-red)](https://www.youtube.com/watch?v=uqRSc-KSA1Y) [![HuggingFace Model](https://img.shields.io/badge/HF%20Model-Weights-yellow)](https://huggingface.co/patrickjohncyh/fashion-clip) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Z1hAxBnWjF76bEi9KQ6CMBBEmI_FVDrW?usp=sharing) [![Medium Blog Post](https://raw.githubusercontent.com/aleen42/badges/master/src/medium.svg)](https://towardsdatascience.com/teaching-clip-some-fashion-3005ac3fdcc3) [![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://huggingface.co/spaces/vinid/fashion-clip-app)")
-
-
-st.write("This web app uses FashionCLIP, our new foundation model for fashion, to find clothing items based on a query of the item you want to find.")
-
-st.write("The model is going to find the most similar item to your query, given a list of 5000 items that have been released by Alexey Grigorev [here](https://github.com/alexeygrigorev/clothing-dataset).")
-st.write("Note that some queries might not return anything useful. This could be both due to model's limitation or to the fact that the item you are looking for is missing from the collection.")
-st.write("You can find more about FashionCLIP on the [repo](https://github.com/patrickjohncyh/fashion-clip) or on our [paper](https://www.nature.com/articles/s41598-022-23052-9)")
+from streamlit_image_select import image_select
+os.environ["CUDA_VISIBLE_DEVICES"] =""
+import torch
+torch.cuda.is_available = lambda : False
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+st.sidebar.write("# Shoping muse")
+
+#query = st.sidebar.text_input("Enter some text", "A red dress")
+#prompt = st.chat_input("Say something")
+st.write("Shoping MUSE")
+
+def horizontal_scroll_images(images):
+    with st.beta_container():
+        for img_path in images:
+            st.image(img_path, use_column_width=True)
+            
+def horizontal_scroll_images(images,image_width=300):
+    cols = st.columns(len(images))
+    for col, img_path in zip(cols, images):
+        
+        col.image(img_path, use_column_width=True)
+        
+#def horizontal_scroll_images(images, image_width=300):
+#    cols = st.columns(len(images))
+#    for col, img_path in zip(cols, images):
+#        col.image(img_path, width=image_width)             
+
+
+new_size = (800, 600)  # Set your desired width and height
 
 @st.cache_resource
 def load_embedding_file():
@@ -43,43 +49,94 @@ fclip = FashionCLIP('fashion-clip')
 if not os.path.exists("clothing-dataset"):
     subprocess.run("git clone https://github.com/alexeygrigorev/clothing-dataset", shell=True)
 
-st.write("## Simple FashionCLIP search engine")
-query = st.text_input("Enter a description of the clothing item you want to find", "a red dress")
-
+#st.write("## Simple FashionCLIP search engine")
+#query = st.text_input("Enter a description of the clothing item you want to find", "a red dress")
+#query = prompt
 images, image_embeddings = load_embedding_file()
+image_cnt=8
+def append_message(sender, message):
+    chat_history.append((sender, message))
+
+def chatbot_interface():
+    st.sidebar.title("Chatbot Interface")
+
+    user_input = st.sidebar.text_input("You:", key="user_input")
+
+    if st.sidebar.button("Send"):
+        append_message("You", user_input)
+        # Replace the following line with your chatbot logic to generate a response
+        append_message("Chatbot", f"Bot response to: {user_input}")
+
+
+    query=user_input
+    text_embedding = fclip.encode_text([query], 32)[0]
+    arr=text_embedding.dot(image_embeddings.T)
+    id_of_matched_object1=(-arr).argsort()[:image_cnt]
+    id_of_matched_object = np.argmax(arr)
+
+    image = Image.open(images[id_of_matched_object])
+    #st.image(image)
+    image=[]
+    for k in id_of_matched_object1:
+    	image.append(Image.open(images[k]).resize(new_size))
+    img = image_select(
+    label="Results",
+    images=image,
+    captions=[str(query) + "result  "] * (image_cnt),
+	)
+
+    st.sidebar.markdown("---")
+
+    # Display the chat history
+    st.sidebar.title("Chat History")
+
+    for sender, message in chat_history:
+        st.sidebar.text(f"{sender}: {message}")
+
+# Initialize the chat history
+chat_history = []
+
+# Main content area
+st.title("Muse Chatbot")
+
+# Display the chatbot interface inside a box in the sidebar
+st.sidebar.markdown("## Chatbot Box")
+
+
+
+
+#text_embedding = fclip.encode_text([query], 32)[0]
+#arr=text_embedding.dot(image_embeddings.T)
+#id_of_matched_object1=(-arr).argsort()[:image_cnt]
+#id_of_matched_object = np.argmax(arr)
+
+#image = Image.open(images[id_of_matched_object])
+#st.image(image)
+#image=[]
+
+
+
+#for k in id_of_matched_object1:
+#	image.append(Image.open(images[k]).resize(new_size))
+
+
+#img = image_select(
+#    label="Results",
+#    images=image,
+#    captions=[str(query) + "result  "] * (image_cnt),
+#)
+
+	
+#st.title("Horizontal Scroll of Images")
+
+# Specify the width of the images
+#image_width = 300
+
+#horizontal_scroll_images(image)
+#print(image)
+#st.image(image , use_column_width=True, caption=["some generic text"] * (image_cnt))
+
+
+chatbot_interface()
 
-text_embedding = fclip.encode_text([query], 32)[0]
-
-id_of_matched_object = np.argmax(text_embedding.dot(image_embeddings.T))
-
-image = Image.open(images[id_of_matched_object])
-
-st.image(image)
-
-
-st.write("If you use FashionCLIP in your work, please cite our paper:")
-st.write("""
-```
-@Article{Chia2022,
-    title="Contrastive language and vision learning of general fashion concepts",
-    author="Chia, Patrick John
-            and Attanasio, Giuseppe
-            and Bianchi, Federico
-            and Terragni, Silvia
-            and Magalh{\~a}es, Ana Rita
-            and Goncalves, Diogo
-            and Greco, Ciro
-            and Tagliabue, Jacopo",
-    journal="Scientific Reports",
-    year="2022",
-    month="Nov",
-    day="08",
-    volume="12",
-    number="1",
-    pages="18958",
-    issn="2045-2322",
-    doi="10.1038/s41598-022-23052-9",
-    url="https://doi.org/10.1038/s41598-022-23052-9"
-```
-}""")