Create app.py

app.py

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+import streamlit as st
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+# Load the GPT-2 model and tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "gpt2"
+    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+    model = GPT2LMHeadModel.from_pretrained(model_name)
+    return model, tokenizer
+
+# Function to generate a response from GPT-2
+def generate_response(input_text, model, tokenizer):
+    inputs = tokenizer.encode(input_text, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=150, do_sample=True, top_p=0.9, top_k=50)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Streamlit UI setup
+def main():
+    st.title("GPT-2 Chatbot")
+
+    # Chat history
+    if 'history' not in st.session_state:
+        st.session_state['history'] = []
+
+    user_input = st.text_input("You:", "")
+    
+    # Generate and display response
+    if user_input:
+        model, tokenizer = load_model()
+        response = generate_response(user_input, model, tokenizer)
+        st.session_state['history'].append({"user": user_input, "bot": response})
+    
+    # Display chat history
+    for chat in st.session_state['history']:
+        st.write(f"You: {chat['user']}")
+        st.write(f"Bot: {chat['bot']}")
+
+if __name__ == "__main__":
+    main()