Create app.py (#1)

- Create app.py (795c2c61a21fc02b36da67b9422c52d4ab81f29f)

app.py

@@ -0,0 +1,58 @@
+from langchain.agents import ConversationalChatAgent, AgentExecutor
+from langchain.callbacks import StreamlitCallbackHandler
+from langchain.chat_models import ChatOpenAI
+from langchain import HuggingFaceHub
+from langchain.memory import ConversationBufferMemory
+from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
+from langchain.chains import LLMChain, RetrievalQA
+from langchain import PromptTemplate
+import streamlit as st
+import os
+import dotenv
+
+dotenv.load_dotenv()
+
+HUGGINGFACE_API = os.getenv("HUGGINGFACE_API")
+
+st.set_page_config(page_title="ChatBot", page_icon="😊")
+st.title("ChatBot")
+
+msgs = StreamlitChatMessageHistory()
+memory = ConversationBufferMemory(
+    chat_memory=msgs, return_messages=True, memory_key="chat_history", output_key="output"
+)
+if len(msgs.messages) == 0:
+    msgs.clear()
+    msgs.add_ai_message("How can I help you?")
+    st.session_state.steps = {}
+
+avatars = {"human": "user", "ai": "assistant"}
+for idx, msg in enumerate(msgs.messages):
+    with st.chat_message(avatars[msg.type]):
+        # Render intermediate steps if any were saved
+        for step in st.session_state.steps.get(str(idx), []):
+            if step[0].tool == "_Exception":
+                continue
+            with st.expander(f"✅ **{step[0].tool}**: {step[0].tool_input}"):
+                st.write(step[0].log)
+                st.write(f"**{step[1]}**")
+        st.write(msg.content)
+
+if prompt := st.chat_input(placeholder="Who won the Women's U.S. Open in 2018?"):
+    st.chat_message("user").write(prompt)
+
+    msgs.add_user_message(prompt)
+    llm = HuggingFaceHub(
+            repo_id="tiiuae/falcon-7b-instruct",
+            model_kwargs={"temperature": 0.5, "max_new_tokens": 500},
+            huggingfacehub_api_token=HUGGINGFACE_API,
+        )
+    prompt_template = PromptTemplate.from_template(
+        "Answer the question: {prompt}"
+    )
+    qa_chain = LLMChain(llm = llm, prompt = prompt_template)
+    with st.chat_message("assistant"):
+        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
+        response = qa_chain({"prompt": prompt})
+        msgs.add_ai_message(response["text"])
+        st.write(response["text"])