gradio app chatbot

app.py

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+from langchain.chat_models import ChatOpenAI
+import gradio as gr
+
+import os
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import DeepLake
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.document_loaders import SeleniumURLLoader
+from langchain import PromptTemplate
+from langchain import OpenAI
+
+os.environ['OPENAI_API_KEY'] = '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'
+os.environ["ACTIVELOOP_TOKEN"] = "eyJhbGciOiJIUzUxMiIsImlhdCI6MTY5NTE5MTAyNiwiZXhwIjoxNzU4MzQ5NDA3fQ.eyJpZCI6InV0a2Fyc2h0aXdhcmkifQ.PK_iz7uybeSmgqFvOYrICw-CQDbDY1aOjYhkMu-0Jle6gU33dCwxah7bmy39O0hPN4jYLu_RfLuU-XejyNvXrw"
+
+llm = ChatOpenAI(temperature=1.0, model='gpt-3.5-turbo-0613')
+
+# URLs of articles to scrape
+urls = ["https://modelwise.ai/product/","https://modelwise.ai/category/blog-articles/","https://modelwise.ai/company/"]
+
+# Load documents using Selenium
+loader = SeleniumURLLoader(urls=urls)
+docs_not_splitted = loader.load()
+
+# Split documents into smaller chunks
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+docs = text_splitter.split_documents(docs_not_splitted)
+
+# Create OpenAIEmbeddings instance
+embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")
+
+# Specify your ActiveLoop organization ID
+my_activeloop_org_id = "utkarshtiwari"
+my_activeloop_dataset_name = "chatbot_modelwise"
+dataset_path = f"hub://{my_activeloop_org_id}/{my_activeloop_dataset_name}"
+db = DeepLake(dataset_path=dataset_path, embedding_function=embeddings)
+
+# Add documents to the Deep Lake dataset
+db.add_documents(docs)
+
+template = """You are an exceptional customer support chatbot for the company Modelwise that gently answers questions related to the company.
+
+You know the following context information.
+
+{chunks_formatted}
+
+Answer the following question from a customer. Use only information from the context. If you don't know the answer just ask the customer to contact Arnold and provide  his contact details. Do not make up any answer.
+
+Question: {query}
+
+Answer:"""
+
+# Create a PromptTemplate instance
+prompt = PromptTemplate(
+    input_variables=["chunks_formatted", "query"],
+    template=template,
+)
+
+def predict(query):
+
+    # Retrieve relevant chunks from the Knowledge Base
+    docs = db.similarity_search(query)
+    retrieved_chunks = [doc.page_content for doc in docs]
+
+    # Format the prompt with retrieved chunks and user query
+    chunks_formatted = "\n\n".join(retrieved_chunks)
+    prompt_formatted = prompt.format(chunks_formatted=chunks_formatted, query=query)
+    # Create an OpenAI instance for text generation
+    llm = OpenAI(model="text-davinci-003", temperature=0)
+
+    # Generate the answer using GPT-3
+    answer = llm(prompt_formatted)
+    print(answer)
+    return answer
+
+gr.ChatInterface(predict).launch()