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import streamlit as st
import requests
import json
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load model and tokenizer
@st.cache_resource
def load_model():
model = AutoModelForSeq2SeqLM.from_pretrained("shreyanshjha0709/watch-description-generator")
tokenizer = AutoTokenizer.from_pretrained("shreyanshjha0709/watch-description-generator")
return model, tokenizer
model, tokenizer = load_model()
# Load the JSON file from a URL
@st.cache_data
def load_json_from_url(url):
response = requests.get(url)
return response.json()
# Provide your JSON URL here
json_url = "https://www.ethoswatches.com/feeds/holbox_ai.json"
data = load_json_from_url(json_url)
# Extract unique brands
brands = sorted(list(set([item["brand"] for item in data])))
# Streamlit UI
st.title("Watch Description Generator")
# Select brand
selected_brand = st.selectbox("Select a Brand", ["Select"] + brands)
# Filter watches and SKUs by the selected brand
if selected_brand != "Select":
watches = [item["name"] for item in data if item["brand"] == selected_brand]
skus = [item["sku"] for item in data if item["brand"] == selected_brand]
selected_watch = st.selectbox("Select Watch Name (Optional)", ["Select"] + watches)
selected_sku = st.selectbox("Select SKU (Optional)", ["Select"] + skus)
# Get the selected watch data from the JSON
watch_data = None
if selected_watch != "Select":
watch_data = next((item for item in data if item["name"] == selected_watch), None)
elif selected_sku != "Select":
watch_data = next((item for item in data if item["sku"] == selected_sku), None)
if watch_data:
# Generate description based on attributes
if st.button("Generate Description"):
attributes = {
"brand": watch_data["brand"],
"name": watch_data.get("name", "Unknown Watch"),
"sku": watch_data.get("sku", "Unknown SKU"),
"features": watch_data.get("features", "Unknown Features"),
"casesize": watch_data.get("casesize", "Unknown Case Size"),
"movement": watch_data.get("movement", "Unknown Movement"),
"gender": watch_data.get("gender", "Unknown Gender"),
}
input_text = f"Brand: {attributes['brand']}, Watch Name: {attributes['name']}, SKU: {attributes['sku']}, Features: {attributes['features']}, Case Size: {attributes['casesize']}, Movement: {attributes['movement']}, Gender: {attributes['gender']}"
# Tokenize input and generate description
inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
outputs = model.generate(**inputs, max_length=150, num_return_sequences=1)
# Decode generated text
description = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Display the result
st.write("### Generated Description")
st.write(description)
# Display watch details
st.write("### Watch Details")
st.json(json.dumps(watch_data, indent=2))
else:
st.warning("Please select a brand.")
# Add some information about the app
st.sidebar.title("About")
st.sidebar.info(
"This app uses a fine-tuned AI model to generate descriptions for watches. "
"Select a brand and a watch to get started. The model will generate a unique "
"description based on the watch's attributes."
)
# Add a footer
st.markdown(
"""
<style>
.footer {
position: fixed;
left: 0;
bottom: 0;
width: 100%;
background-color: #f1f1f1;
color: black;
text-align: center;
}
</style>
<div class="footer">
<p>Developed with ❤️ by Your Name</p>
</div>
""",
unsafe_allow_html=True
) |