feat: add app.py and images

app.py

@@ -0,0 +1,89 @@
+import streamlit as st
+from huggingface_hub import InferenceClient
+from langchain import HuggingFaceHub
+import requests
+# Internal usage
+import os
+from dotenv import load_dotenv
+from time import sleep
+
+load_dotenv()
+# Set HF API token
+HUGGINGFACEHUB_API_TOKEN = os.getenv('HF_TOKEN')
+
+#AVATARS
+av_us = './man.png'  #"🦖"  #A single emoji, e.g. "🧑‍💻", "🤖", "🦖". Shortcodes are not supported.
+av_ass = './robot.png'
+
+# FUNCTION TO LOG ALL CHAT MESSAGES INTO chathistory.txt
+def writehistory(text):
+    with open('chathistory.txt', 'a') as f:
+        f.write(text)
+        f.write('\n')
+    f.close()
+
+repo="HuggingFaceH4/starchat-beta"
+
+### START STREAMLIT UI
+st.title("🤗 AI 聊天機器人 測試版")
+st.subheader("支援中文對話")
+
+# Set a default model
+if "hf_model" not in st.session_state:
+    st.session_state["hf_model"] = "HuggingFaceH4/starchat-beta"
+
+### INITIALIZING STARCHAT FUNCTION MODEL
+def starchat(model,myprompt, your_template):
+    from langchain import PromptTemplate, LLMChain
+    os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN
+    llm = HuggingFaceHub(repo_id=model , 
+                         model_kwargs={"min_length":30,
+                                       "max_new_tokens":1024, "do_sample":True, 
+                                       "temperature":0.2, "top_k":50, 
+                                       "top_p":0.95, "eos_token_id":49155})
+    template = your_template
+    prompt = PromptTemplate(template=template, input_variables=["myprompt"])
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    llm_reply = llm_chain.run(myprompt)
+    reply = llm_reply.partition('<|end|>')[0]
+    return reply
+
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    if message["role"] == "user":
+        with st.chat_message(message["role"],avatar=av_us):
+            st.markdown(message["content"])
+    else:
+        with st.chat_message(message["role"],avatar=av_ass):
+            st.markdown(message["content"])
+
+# Accept user input
+if myprompt := st.chat_input("請介紹台灣"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": myprompt})
+    # Display user message in chat message container
+    with st.chat_message("user", avatar=av_us):
+        st.markdown(myprompt)
+        usertext = f"user: {myprompt}"
+        writehistory(usertext)
+        # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        full_response = ""
+        res  =  starchat(
+                st.session_state["hf_model"],
+                myprompt, "<|system|>\n<|end|>\n<|user|>\n{myprompt}<|end|>\n<|assistant|>")
+        response = res.split(" ")
+        for r in response:
+            full_response = full_response + r + " "
+            message_placeholder.markdown(full_response + "▌")
+            sleep(0.1)
+        message_placeholder.markdown(full_response)
+        asstext = f"assistant: {full_response}"
+        writehistory(asstext)       
+        st.session_state.messages.append({"role": "assistant", "content": full_response})