Create app.py

app.py

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+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+torch.random.manual_seed(0)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/Phi-3-mini-128k-instruct", 
+    device_map="cpu", 
+    torch_dtype="auto", 
+    trust_remote_code=True, 
+)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct")
+
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+)
+
+generation_args = {
+    "max_new_tokens": 500,
+    "return_full_text": False,
+    "temperature": 0.0,
+    "do_sample": False,
+}
+
+
+
+
+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})
+
+    ##Get response to the message using client
+    output = pipe(messages, **generation_args)
+
+    msg = 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})