Update app.py

app.py

@@ -1,33 +1,34 @@
-import transformers
-import torch 
+import streamlit as st
+import torch
 from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
 
-# Load tokenizer and model
-tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
-
-# Define a function to preprocess user input
-def preprocess_input(text):
-  encoded_input = tokenizer(text, return_tensors='pt')
-  return encoded_input
-
-# Define a function to generate response based on user input
-def generate_response(user_input):
-  encoded_input = preprocess_input(user_input)
-  outputs = model(**encoded_input)
-  # Extract relevant information from model outputs (e.g., predicted class)
-  # Based on the extracted information, formulate a response using predefined responses or logic
-  response = "I'm still under development, but I understand you said: {}".format(user_input)
-  return response
-
-# Start the chat loop
-while True:
-  # Get user input
-  line = input("Enter your input: ")
-
-  if line.lower() == "quit": 
-    break
-
-  # Generate response based on user input
-  bot_response = generate_response(line)  # Assuming generate_response is defined elsewhere
-  print("Bot:", bot_response)
+# Title and Description
+st.title("Simple DistilBERT Chatbot")
+st.write("This is a basic chatbot prototype. Ask it something!")
+
+# Load Model and Tokenizer
+@st.cache_resource  # Cache for efficiency
+def load_model_tokenizer():
+    tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+    model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
+    return tokenizer, model
+
+tokenizer, model = load_model_tokenizer()
+
+# User Input
+user_input = st.text_input("You: ")
+
+# Generate Response on Button Click
+if st.button("Send"):
+    if not user_input:
+        st.warning("Please enter some text.")
+    else:
+        # Preprocess and Generate Response (placeholder)
+        encoded_input = preprocess_input(user_input)  
+        outputs = model(**encoded_input)  
+
+        # (TODO) Extract relevant info from outputs 
+
+        bot_response = "I'm still under development, but I understand you said: {}".format(user_input)
+        st.write("Bot: " + bot_response) 
+