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streamlit_app.py

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+from dataclasses import dataclass
+from typing import Literal
+import streamlit as st
+import os
+from llamaapi import LlamaAPI
+from langchain_experimental.llms import ChatLlamaAPI
+from langchain.embeddings import HuggingFaceEmbeddings
+import pinecone
+from langchain.vectorstores import Pinecone
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+import streamlit.components.v1 as components
+
+HUGGINGFACEHUB_API_TOKEN = st.secrets['HUGGINGFACEHUB_API_TOKEN']
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN
+
+
+@dataclass
+class Message:
+    """Class for keeping track of a chat message."""
+    origin: Literal["human", "ai"]
+    message: str
+
+
+def load_css():
+    with open("static/styles.css", "r") as f:
+        css = f"<style>{f.read()}</style>"
+        st.markdown(css, unsafe_allow_html=True)
+
+
+def download_hugging_face_embeddings():
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
+    return embeddings
+
+
+def initialize_session_state():
+    if "history" not in st.session_state:
+        st.session_state.history = []
+    if "conversation" not in st.session_state:
+        llama = LlamaAPI(st.secrets["LlamaAPI"])
+        model = ChatLlamaAPI(client=llama)
+
+        embeddings = download_hugging_face_embeddings()
+
+        # Initializing the Pinecone
+        pinecone.init(
+            api_key=st.secrets["PINECONE_API_KEY"],  # find at app.pinecone.io
+            environment=st.secrets["PINECONE_API_ENV"]  # next to api key in console
+        )
+        index_name = "legal-advisor"  # put in the name of your pinecone index here
+
+        docsearch = Pinecone.from_existing_index(index_name, embeddings)
+
+        prompt_template = """
+            You are a trained bot to guide people about Indian Law. You will answer user's query with your knowledge and the context provided. 
+            If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
+            Do not say thank you and tell you are an AI Assistant and be open about everything.
+            Use the following pieces of context to answer the users question.
+            Context: {context}
+            Question: {question}
+            Only return the helpful answer below and nothing else.
+            Helpful answer:
+            """
+
+        PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+        chain_type_kwargs = {"prompt": PROMPT}
+        retrieval_chain = RetrievalQA.from_chain_type(llm=model,
+                                                      chain_type="stuff",
+                                                      retriever=docsearch.as_retriever(
+                                                          search_kwargs={'k': 2}),
+                                                      return_source_documents=True,
+                                                      chain_type_kwargs=chain_type_kwargs)
+
+        st.session_state.conversation = retrieval_chain
+
+
+def on_click_callback():
+    human_prompt = st.session_state.human_prompt
+    response = st.session_state.conversation(
+        human_prompt
+    )
+    llm_response = response['result']
+    print(llm_response)
+    st.session_state.history.append(
+        Message("human", human_prompt)
+    )
+    st.session_state.history.append(
+        Message("ai", llm_response)
+    )
+
+
+load_css()
+initialize_session_state()
+
+st.title("Hello Custom CSS Chatbot 🤖")
+
+chat_placeholder = st.container()
+prompt_placeholder = st.form("chat-form")
+
+with chat_placeholder:
+    for chat in st.session_state.history:
+        div = f"""
+    <div class="chat-row 
+        {'' if chat.origin == 'ai' else 'row-reverse'}">
+        <img class="chat-icon" src="app/static/{
+        'ai_icon.png' if chat.origin == 'ai'
+        else 'user_icon.png'}"
+             width=32 height=32>
+        <div class="chat-bubble
+        {'ai-bubble' if chat.origin == 'ai' else 'human-bubble'}">
+            &#8203;{chat.message}
+        </div>
+    </div>
+            """
+        st.markdown(div, unsafe_allow_html=True)
+
+    for _ in range(3):
+        st.markdown("")
+
+with prompt_placeholder:
+    st.markdown("**Chat**")
+    cols = st.columns((6, 1))
+    cols[0].text_input(
+        "Chat",
+        value="Hello bot",
+        label_visibility="collapsed",
+        key="human_prompt",
+    )
+    cols[1].form_submit_button(
+        "Submit",
+        type="primary",
+        on_click=on_click_callback,
+    )
+
+components.html("""
+<script>
+const streamlitDoc = window.parent.document;
+
+const buttons = Array.from(
+    streamlitDoc.querySelectorAll('.stButton > button')
+);
+const submitButton = buttons.find(
+    el => el.innerText === 'Submit'
+);
+
+streamlitDoc.addEventListener('keydown', function(e) {
+    switch (e.key) {
+        case 'Enter':
+            submitButton.click();
+            break;
+    }
+});
+</script>
+""",
+                height=0,
+                width=0,
+                )