Update app.py

app.py

@@ -1,10 +1,9 @@
 import streamlit as st
 from transformers import T5ForConditionalGeneration, T5Tokenizer
 import torch
-import random
 
 # Load pre-trained T5 model and tokenizer
-model_name = "t5-small"  # You can use "t5-base" or "t5-large" for better quality but slower response
+model_name = "t5-small"  # Use t5-small for faster responses
 model = T5ForConditionalGeneration.from_pretrained(model_name)
 tokenizer = T5Tokenizer.from_pretrained(model_name)
 
@@ -12,64 +11,25 @@ tokenizer = T5Tokenizer.from_pretrained(model_name)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
-# Initialize chat history and conversation context
-if 'history' not in st.session_state:
-    st.session_state['history'] = []
-if 'conversation' not in st.session_state:
-    st.session_state['conversation'] = []
-
-# Define multiple system prompts to control bot's behavior
-system_prompts = [
-    "You are a fun, casual chatbot. Keep the conversation light-hearted and interesting.",
-    "You are a friendly assistant. Respond in a polite, friendly, and informative manner.",
-    "You are an informative assistant. Respond clearly and concisely to any questions asked.",
-    "You are a compassionate listener, always responding with kindness and empathy."
-]
-
-# Select a random system prompt to start the conversation
-def get_system_prompt():
-    return random.choice(system_prompts)
+# Streamlit Interface
+st.title("Simple Chatbot with T5")
 
 def generate_response(input_text):
-    # If it's the first interaction, add the system prompt to the conversation history
-    if len(st.session_state['history']) == 0:
-        system_prompt = get_system_prompt()
-        st.session_state['conversation'].append(f"System: {system_prompt}")
-        system_input = f"conversation: {system_prompt} "
-        st.session_state['history'].append(system_input)
-
-    # Prepare the user input by appending it to the history
-    user_input = f"conversation: {input_text} "
-
-    # Concatenate history (only user input after system prompt)
-    full_input = "".join(st.session_state['history']) + user_input
-
-    # Tokenize input text and generate response from the model
-    input_ids = tokenizer.encode(full_input, return_tensors="pt").to(device)
-    outputs = model.generate(input_ids, max_length=1000, num_beams=5, top_p=0.95, temperature=0.7, pad_token_id=tokenizer.eos_token_id)
-
-    # Decode the model's output
+    # Prepare the input (as T5 expects a task-based prompt, we'll just prefix with 'translate English to English' for simplicity)
+    input_text = f"conversation: {input_text}"
+
+    # Tokenize input text
+    input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)
+    
+    # Generate a response from the model
+    outputs = model.generate(input_ids, max_length=100, num_beams=5, top_p=0.95, temperature=0.7, pad_token_id=tokenizer.eos_token_id)
+    
+    # Decode the model's output to a readable string
     bot_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-    # Update the history with the new user input and the model's output
-    st.session_state['history'].append(user_input)
-    st.session_state['history'].append(f"bot: {bot_output} ")
-
-    # Add both user input and bot response to the conversation history for display
-    st.session_state['conversation'].append(f"You: {input_text}")
-    st.session_state['conversation'].append(f"Bot: {bot_output}")
-
     return bot_output
 
-# Streamlit Interface
-st.title("Chat with T5")
-
-# Display the conversation history
-if st.session_state['conversation']:
-    for message in st.session_state['conversation']:
-        st.markdown(f"<p style='color:gray; padding:5px;'>{message}</p>", unsafe_allow_html=True)
-
-# Create input box for user
+# Create input box for user to type a message
 user_input = st.text_input("You: ", "")
 
 if user_input: