Create app.py

app.py

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+#from openai import OpenAI
+import streamlit as st
+
+# import torch
+# from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+# from threading import Thread
+
+# from transformers import AutoTokenizer, AutoModelForCausalLM
+# from sentence_transformers import SentenceTransformer
+# from langchain_community.document_loaders import TextLoader
+# from langchain_community.embeddings.sentence_transformer import (
+#     SentenceTransformerEmbeddings,
+# )
+# from langchain_community.vectorstores import Chroma
+# from langchain_text_splitters import CharacterTextSplitter
+
+# from transformers import pipeline
+
+# tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it")
+# model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it")
+# pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=512)
+# hf_model = HuggingFacePipeline(pipeline=pipe
+
+
+from langchain_community.llms import HuggingFaceHub
+
+llm = HuggingFaceHub(
+    repo_id="google/gemma-2b-it",
+    task="text-generation",
+    model_kwargs={
+        "max_new_tokens": 512,
+        "top_k": 30,
+        "temperature": 0.1,
+        "repetition_penalty": 1.03
+    },
+)
+
+
+from langchain.schema import (
+    HumanMessage,
+    SystemMessage,
+)
+from langchain_community.chat_models.huggingface import ChatHuggingFace
+
+messages = [
+    SystemMessage(content="You're a helpful assistant"),
+    HumanMessage(
+        content=""
+    ),
+]
+
+chat_model = ChatHuggingFace(llm=llm)
+
+
+# from dotenv import load_dotenv
+# import os
+
+# load_dotenv()
+
+# openai_api_key = os.getenv("OPENAI_API_KEY")
+
+# with st.sidebar:
+#     openai_api_key = st.text_input("OpenAI API Key", key="chatbot_api_key", type="password")
+#     "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
+#     "[View the source code](https://github.com/streamlit/llm-examples/blob/main/Chatbot.py)"
+#     "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
+
+st.title("💬 Chatbot")
+st.caption("🚀 A streamlit chatbot powered by OpenAI LLM")
+if 'messages' not in st.session_state:
+    st.session_state['messages'] = [] #[{"role": "assistant", "content": "How can I help you?"}]
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg["content"])
+
+# from google.colab import userdata
+# openai_api_key = userdata.get('OPENAI_API_KEY')
+
+
+if prompt := st.chat_input():
+    # if not openai_api_key:
+    #     st.info("Please add your OpenAI API key to continue.")
+    #     st.stop()
+
+    #client = OpenAI(api_key=openai_api_key)
+    #client = OpenAI()
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    st.chat_message("user").write(prompt)
+    #response = client.chat.completions.create(model="gpt-3.5-turbo", messages=st.session_state.messages)
+    res = chat_model.invoke(messages)
+    #msg = response.choices[0].message.content
+    msg = res.content
+    st.session_state.messages.append({"role": "assistant", "content": msg})
+    st.chat_message("assistant").write(msg)