Create app.py

app.py

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+import gradio as gr
+import pinecone
+import openai
+import os
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.vectorstores import Pinecone 
+
+
+BOOK_TOKEN = os.getenv("book")
+pine = os.getenv("pine")
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+os.environ["OPENAI_API_KEY"] = BOOK_TOKEN
+
+OPENAI_API_KEY = ""
+PINECONE_API_KEY = ""
+PINECONE_API_ENV = "us-east-1-aws"
+
+#embedding = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEYs)
+embed_model = "text-embedding-ada-002"
+
+pinecone.init(
+    api_key=pine,
+    environment=PINECONE_API_ENV
+)
+openai.api_key=BOOK_TOKEN
+index_n = "ibc-12"
+index = pinecone.Index(index_n)
+index.describe_index_stats()
+
+limit = 3750
+
+llm = ChatOpenAI(temperature=0, model_name="gpt-4" )
+
+embeddings = OpenAIEmbeddings(
+    model="text-embedding-ada-002"
+)
+
+#get the db index
+db = Pinecone.from_existing_index(index_name=index_n, embedding=embeddings)
+
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(label="Talk to the Book")
+    msg = gr.Textbox()
+    clear = gr.Button("Clear")
+    chat_history = []
+    
+    def user(user_message, chat_history):
+
+        memory = ConversationBufferMemory(
+            memory_key='chat_history',
+            return_messages=False
+        )
+    
+        #Initalize lanchain - Conversation Retrieval Chain
+        qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0), retriever=db.as_retriever(), memory=memory)
+
+    
+
+        #get response from QA Chain
+        response = qa({'question': user_message, "chat_history": chat_history})
+        #append user message and respone to chat history
+        chat_history.append((user_message, response["answer"]))
+        return gr.update(value=""), chat_history
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False)
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+if __name__ == "__main__":
+  demo.launch(debug=True)