Upload app.py

app.py

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+
+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;
+    }
+    .menu {
+        font-size:18px !important;
+        line-height: 1.8;
+        font-family: 'Arial', sans-serif;
+    }
+    </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)
+
+    # Display the predicted label with its corresponding sentiment
+    label_map = {0: "Negative", 1: "Neutral", 2: "Positive"}
+
+    # Display the predicted label and corresponding sentiment
+    st.write(f"Predicted label is {predicted_label} ({label_map.get(predicted_label, 'Unknown')} Sentence)")
+
+    # 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)
+
+    # Correcting the misplaced closing parenthesis
+    st.write(responses.get(predicted_label, "I'm not sure what you're asking for."))
+
+    # Add a separator
+    st.markdown('<div class="divider"></div>', unsafe_allow_html=True)