Delete app.py

app.py

@@ -1,80 +0,0 @@
-
-import streamlit as st
-import torch
-import numpy as np
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-# Load the model and tokenizer from Hugging Face
-model = AutoModelForSequenceClassification.from_pretrained("Caseyishere/StoryCraft", num_labels=5)
-tokenizer = AutoTokenizer.from_pretrained("Caseyishere/StoryCraft")
-
-# Streamlit app interface
-st.set_page_config(page_title="Story Craft", page_icon="🍽️", layout="centered")
-
-# Set page title and styles
-st.title("🍽️ Welcome to Story Craft 🍽️")
-st.markdown("""
-    <style>
-    .big-font {
-        font-size:24px !important;
-        font-weight:bold;
-    }
-    .highlight {
-        color: #FF4B4B;
-    }
-    .divider {
-        border-top: 2px solid #bbb;
-        margin: 20px 0;
-    }
-    </style>
-    """, unsafe_allow_html=True)
-
-# Get user input
-user_input = st.text_input("Please tell us what you like today:")
-
-if user_input:
-    # Preprocess the input using the tokenizer
-    inputs = tokenizer(user_input, padding=True, truncation=True, return_tensors='pt')
-
-    # Get predictions from the model
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-    predictions = predictions.cpu().detach().numpy()
-
-    # Get the predicted label
-    predicted_label = np.argmax(predictions)
-
-    # Generate response based on predicted label
-    responses = {
-        0: '''Appetizer: Escargots: Snails cooked in garlic butter with herbs
-Main Course: Coq au vin: Chicken braised in red wine with mushrooms and onions
-Side Dish: Pommes frites: French fries
-Dessert: Crème brûlée: Custard topped with caramelized sugar
-Beverage: Bordeaux: A red wine from the Bordeaux region of France
-Cheese Course: Fromage à raclette: Melted cheese served with bread, potatoes, and pickles''',
-        1: '''Appetizer: Spätzle: Swabian egg noodles with cheese
-Main Course: Wiener schnitzel: Breaded veal cutlet
-Side Dish: Sauerkraut: Fermented cabbage
-Dessert: Schwarzwälder Kirschtorte: Black Forest cake
-Beverage: Kölsch: A light, golden ale from Cologne
-Cheese Course: Käsekuchen: German cheesecake''',
-        2: '''Appetizer: Creamy Spinach and Artichoke Dip with tortilla chips
-Main Course: Ribeye Steak cooked to your desired temperature (medium-rare, medium, well-done)
-Side Dish: Baked Potato topped with butter, sour cream, and bacon bits
-Dessert: Chocolate Lava Cake with vanilla ice cream
-Beverage: Red Wine (ask your server for a recommendation based on your preferences)
-Salad: Caesar Salad with romaine lettuce, croutons, Parmesan cheese, and Caesar dressing
-Soup: French Onion Soup with caramelized onions, Gruyère cheese, and croutons''',
-        3: "Oops! Something went wrong!"
-    }
-
-    # Display the response based on the predicted label
-    st.markdown('<div class="divider"></div>', unsafe_allow_html=True)
-    st.markdown(f'<div class="big-font highlight">Here is your curated menu based on your input:</div>', unsafe_allow_html=True)
-    st.write(responses.get(predicted_label, "I'm not sure what you're asking for."))
-
-    # Display the predicted label for reference
-    st.write(f"Predicted label is {predicted_label}")
-
-    # Add a separator
-    st.markdown('<div class="divider"></div>', unsafe_allow_html=True)