Upload app.py

app.py

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+import streamlit as st
+
+from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+from langchain.schema import HumanMessage, SystemMessage, AIMessage
+
+st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
+st.header("Hey, I'm your DeepSeek")
+
+if "sessionMessages" not in st.session_state:
+    st.session_state.sessionMessages = [
+        SystemMessage(content="You are a helpful assistant.")
+    ]
+
+
+def load_answer(question):
+    st.session_state.sessionMessages.append(HumanMessage
+                                            (content=question))
+    assistant_answer = chat.invoke(st.session_state.sessionMessages)
+    st.session_state.sessionMessages.append(AIMessage(content=assistant_answer))
+    return assistant_answer.content
+
+def get_text():
+    input_text = st.text_input("You: ")
+    return input_text
+
+llm = HuggingFaceEndpoint(
+    repo_id="HuggingFaceH4/zephyr-7b-beta",
+    task="text-generation",
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.7,
+    repetition_penalty=1.2,
+)
+
+chat_model = ChatHuggingFace(llm=llm)
+
+user_input = get_text()
+submit = st.button('Generate')
+
+if submit:
+    response = load_answer(user_input)
+    st.subheader("Answer: ")
+    st.write(response)