editors-note / app.py
shreyanshjha0709's picture
Update app.py
bf2578c verified
raw
history blame
3.01 kB
import streamlit as st
import requests
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Load model and tokenizer
@st.cache_resource
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
@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"),
# 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.")