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import streamlit as st | |
import requests | |
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
# Load model and tokenizer | |
def load_model(): | |
model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large") | |
tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large") | |
return model, tokenizer | |
model, tokenizer = load_model() | |
# Load the JSON file from a URL | |
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"), | |
# Add more attributes as needed | |
} | |
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") | |
outputs = model.generate(**inputs) | |
# Decode generated text | |
description = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
# Display the result | |
st.write("### Generated Description") | |
st.write(description) | |
else: | |
st.warning("Please select a brand.") | |