Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+import streamlit as st
+
+model_id = "google/gemma-1.1-2b-it"
+dtype = torch.bfloat16
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="cpu",
+    torch_dtype=dtype,
+)
+
+
+
+
+
+
+st.title("💬 Chatbot")
+st.caption("🚀 A streamlit chatbot powered by Microsoft Phi-3-mini")
+
+# Initialize chat history
+if 'messages' not in st.session_state:
+    st.session_state['messages'] = [] #[{"role": "assistant", "content": "How can I help you?"}]
+
+# Display chat messages from history on app rerun
+for messasge in st.session_state.messages:
+    st.chat_message(messasge["role"]).write(messasge["content"])
+
+# React to user input
+if prompt := st.chat_input():
+    
+     # Display user message in chat message container
+    st.chat_message("user").write(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    messages=st.session_state.messages
+
+    
+    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+    ##Get response to the message using client
+    inputs = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")
+    outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
+
+    msg = outputs #output[0]['generated_text']
+    
+     # Display assistant response in chat message container
+    st.chat_message("assistant").write(msg)
+
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": msg})