Create app.py

app.py

@@ -0,0 +1,36 @@
+
+import streamlit as st
+from streamlit_chat import message as st_message
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+@st.cache_resource 
+def get_models():
+    model_name = "PY007/TinyLlama-1.1B-Chat-v0.3"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return tokenizer, model
+
+
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+st.title("Hello Chatbot")
+
+
+def generate_answer():
+    tokenizer, model = get_models()
+    user_message = st.session_state.input_text
+    inputs = tokenizer(st.session_state.input_text, return_tensors="pt")
+    result = model.generate(**inputs)
+    message_bot = tokenizer.decode(
+        result[0], skip_special_tokens=True
+    )  # .replace("<s>", "").replace("</s>", "")
+
+    st.session_state.history.append({"message": user_message, "is_user": True})
+    st.session_state.history.append({"message": message_bot, "is_user": False})
+
+
+st.text_input("Talk to the bot", key="input_text", on_change=generate_answer)
+
+for i, chat in enumerate(st.session_state.history):
+    st_message(**chat, key=str(i)) #unpacking