Create app.py

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
+from langchain_groq import ChatGroq
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ChatMessageHistory, ConversationBufferMemory
+import time
+
+HUGGINGFACEHUB_API_TOKEN = st.secrets['HUGGINGFACEHUB_API_TOKEN']
+
+@dataclass
+class Message:
+    """Class for keeping track of a chat message."""
+    origin: Literal["👤 Human", "👨🏻‍⚖️ Ai"]
+    message: str
+
+
+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:
+        chat = ChatGroq(temperature=0.5, groq_api_key=st.secrets["Groq_api"], model_name="mixtral-8x7b-32768")
+
+        embeddings = download_hugging_face_embeddings()
+
+        # Initializing 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 = "book-recommendations"  # updated index name for books
+
+        docsearch = Pinecone.from_existing_index(index_name, embeddings)
+
+        prompt_template = """
+            You are an AI trained to recommend books. You will suggest books based on the user's preferences and previous likes. 
+            Please provide insightful recommendations and explain why each book might be of interest to the user.
+            Context: {context}
+            User Preference: {question}
+            Suggested Books:
+            """
+
+        PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+        
+        message_history = ChatMessageHistory()
+        memory = ConversationBufferMemory(
+            memory_key="chat_history",
+            output_key="answer",
+            chat_memory=message_history,
+            return_messages=True,
+            )
+        retrieval_chain = ConversationalRetrievalChain.from_llm(llm=chat,
+                                                      chain_type="recommendation",
+                                                      retriever=docsearch.as_retriever(
+                                                          search_kwargs={'k': 5}),
+                                                      return_source_documents=True,
+                                                      combine_docs_chain_kwargs={"prompt": PROMPT},
+                                                      memory=memory
+                                                     )
+
+        st.session_state.conversation = retrieval_chain
+
+
+def on_click_callback():
+    human_prompt = st.session_state.human_prompt
+    st.session_state.human_prompt=""
+    response = st.session_state.conversation(
+        human_prompt
+    )
+    llm_response = response['answer']
+    st.session_state.history.append(
+        Message("👤 Human", human_prompt)
+    )
+    st.session_state.history.append(
+        Message("👨🏻‍⚖️ Ai", llm_response)
+    )
+
+initialize_session_state()
+
+st.title("AI Book Recommender")
+
+st.markdown(
+   """ 
+   👋 **Welcome to the AI Book Recommender!**
+   Share your favorite genres or books, and I'll recommend your next reads!
+   """
+)
+
+chat_placeholder = st.container()
+prompt_placeholder = st.form("chat-form")
+
+with chat_placeholder:
+    for chat in st.session_state.history:
+        st.markdown(f"{chat.origin} : {chat.message}")
+
+with prompt_placeholder:
+    st.markdown("**Chat**")
+    cols = st.columns((6, 1))
+    cols[0].text_input(
+        "Chat",
+        label_visibility="collapsed",
+        key="human_prompt",
+    )
+    cols[1].form_submit_button(
+        "Submit",
+        type="primary",
+        on_click=on_click_callback,
+    )