Update app.py

app.py

@@ -0,0 +1,36 @@
+import streamlit as st 
+import transformers
+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
+  uinput = st.text_input("Enter your input: ")
+
+  if uinput.lower() == "quit": 
+    break
+
+    
+  # Generate response based on user input
+      
+  bot_response = generate_response(line)  # Assuming generate_response is defined elsewhere
+  print("Bot:", bot_response)