Update app.py

app.py

@@ -1,101 +1,8 @@
-import streamlit as st
-import requests
-from bs4 import BeautifulSoup
-import json
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+# ... (previous code remains the same)
 
-# 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
+# Combine Ethos description and attributes into a prompt
+input_text = f"""Generate a detailed, luxurious 150-word description for the following watch, focusing on its craftsmanship, innovation, and design. Use a style similar to high-end watch editorials, highlighting the watch's unique features and its appeal to connoisseurs:
 
-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])))
-
-# Function to scrape Ethos product description using the specified selector
-def scrape_ethos_description(product_link):
-    try:
-        response = requests.get(product_link)
-        soup = BeautifulSoup(response.text, 'html.parser')
-
-        # Target the specific selector to extract the description
-        description = soup.select_one('#brand_collection > div > div.lHeight_100.spec_editorNotes > div > p:nth-child(5)')
-        if description:
-            return description.get_text(strip=True)
-        else:
-            return "No detailed description available from Ethos."
-    except Exception as e:
-        return f"Error fetching details from Ethos: {str(e)}"
-
-# 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:
-        # Display the image from the JSON
-        image_url = watch_data.get("image", None)
-        if image_url:
-            st.image(image_url, caption=f"{watch_data['name']} Image")
-
-        # Get the Ethos product link for web scraping
-        product_link = watch_data.get("url", None)
-        if product_link:
-            st.write(f"Fetching details from: [Product Page]({product_link})")
-
-            # Scrape Ethos product page for description
-            ethos_description = scrape_ethos_description(product_link)
-            st.write("### Ethos Product Description (Extracted)")
-            st.write(ethos_description)
-        else:
-            st.warning("No Ethos link available for this SKU.")
-
-        # Generate a watch description based on attributes and scraped content
-        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"),
-            "water_resistance": watch_data.get("water_resistance", "Unknown Water Resistance"),
-            "power_reserve": watch_data.get("power_reserve", "Unknown Power Reserve"),
-            "dial_color": watch_data.get("dial_color", "Unknown Dial Color"),
-            "strap_material": watch_data.get("strap_material", "Unknown Strap Material")
-        }
-
-        # Combine Ethos description and attributes into a prompt
-        input_text = f"""Generate a detailed 100-word description for the following watch:
 Brand: {attributes['brand']}
 Name: {attributes['name']}
 SKU: {attributes['sku']}
@@ -111,55 +18,28 @@ Strap Material: {attributes['strap_material']}
 Additional details from Ethos:
 {ethos_description}
 
-Description: Provide a luxurious, detailed description focusing on the craftsmanship, innovation, and design, in a style similar to high-end editorials."""
-
-        # Tokenize input and generate description
-        inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
-        outputs = model.generate(
-            **inputs,
-            max_length=150,  # Output length 100-150 words
-            num_return_sequences=1,
-            temperature=0.7,
-            top_k=50,
-            top_p=0.95,
-            do_sample=True,
-            repetition_penalty=1.2  # Prevent repetition
-        )
-
-        # Decode generated text
-        description = tokenizer.decode(outputs[0], skip_special_tokens=True)
+Description:"""
+
+# Tokenize input and generate description
+inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
+outputs = model.generate(
+    **inputs,
+    max_length=300,  # Increased to allow for longer descriptions
+    min_length=200,  # Ensure a minimum length
+    num_return_sequences=1,
+    temperature=0.8,  # Slightly increased for more creativity
+    top_k=50,
+    top_p=0.95,
+    do_sample=True,
+    repetition_penalty=1.2,  # Prevent repetition
+    length_penalty=1.5  # Encourage longer outputs
+)
 
-        # Display the final generated description
-        st.write("### Final Generated Description")
-        st.write(description)
-else:
-    st.warning("Please select a brand.")
+# Decode generated text
+description = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Sidebar information
-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 and additional details from the Ethos website."
-)
+# Display the final generated description
+st.write("### Final Generated Description")
+st.write(description)
 
-# 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 Shreyansh Jha</p>
-    </div>
-    """,
-    unsafe_allow_html=True
-)
+# ... (rest of the code remains the same)