Create app.py

app.py

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+import langchain
+import gradio as gr
+import random
+import time
+import transformers
+from langchain import HuggingFacePipeline
+from langchain import PromptTemplate, LLMChain
+import os
+import torch
+
+import torch
+from transformers import LlamaForCausalLM, LlamaTokenizer
+
+
+# Hugging Face model_path
+model_id = 'SachinKaushik/docGPT'
+tokenizer = LlamaTokenizer.from_pretrained(model_id)
+model = LlamaForCausalLM.from_pretrained(
+    model_id, torch_dtype=torch.float16, device_map='auto',
+)
+
+
+# set model to eval mode
+model.eval()
+
+# Build HF Transformers pipeline
+pipeline=transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device_map="auto",
+    max_length=768,
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id
+)
+
+# Setup prompt template
+template = PromptTemplate(input_variables=['input'], template='{input}')
+
+# Pass hugging face pipeline to langchain class
+llm = HuggingFacePipeline(pipeline=pipeline)
+
+# Build stacked LLM chain i.e. prompt-formatting + LLM
+chain = LLMChain(llm=llm, prompt=template)
+
+# setup prompt template for an instruction with no input
+prompt = PromptTemplate(
+    input_variables=["query"],
+    template="""You are a helpful AI assistant, you will answer the users query
+with a short but precise answer. If you are not sure about the answer you state
+"I don't know". This is a conversation, not a webpage, there should be ZERO HTML
+in the response.
+
+Remember, Assistant responses are concise. Here is the conversation:
+
+User: {query}
+Assistant: """
+)
+
+# Pass hugging face pipeline to langchain class
+llm = HuggingFacePipeline(pipeline=pipeline)
+
+# Build stacked LLM chain i.e. prompt-formatting + LLM
+llm_chain = LLMChain(llm=llm, prompt=prompt)
+
+
+# import PDF document loaders and splitter
+from langchain.document_loaders import PyPDFLoader, TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+
+# Import chroma as the vector store
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+
+# Import vector store tools
+from langchain.agents.agent_toolkits import (
+    create_vectorstore_agent,
+    VectorStoreToolkit,
+    VectorStoreInfo
+)
+
+# embedding Class
+from langchain.embeddings import HuggingFaceEmbeddings
+
+
+# function to generate embeddings
+from langchain.document_loaders import WebBaseLoader
+
+def load_data_in_VectorDB(data_source,doc_type='text', model_id='intfloat/e5-base-v2', chunk_size=300, chunk_overlap=100):
+    if doc_type=="text":
+        loader = TextLoader(data_source,encoding="utf-8" )
+        documents = loader.load()
+    else:
+        loader = WebBaseLoader(data_source)
+        documents = loader.load()
+
+    text_splitter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=0,add_start_index=True )
+    texts = text_splitter.split_documents(documents)
+    embeddings = HuggingFaceEmbeddings(model_name=model_id)
+    return texts, embeddings
+
+texts, embeddings = load_data_in_VectorDB(data_source='https://en.wikipedia.org/wiki/2022%E2%80%9323_NBA_season',doc_type='web')
+db = Chroma.from_documents(texts, embeddings)
+retriever = db.as_retriever()
+
+# Pass hugging face pipeline to langchain class
+llm = HuggingFacePipeline(pipeline=pipeline)
+
+# qa agent using LLM and Retriever
+qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
+
+import gradio as gr
+
+def generate_answer(query):
+    ans = qa({"query": query})
+    ans = ans['result']
+    meta= "\n".join([i for i in {i.metadata['source'] for i in result['source_documents']}])
+    return f"DocGPT Response: {ans} \nSource: {meta}"
+
+theme = gr.themes.Default(#color contructors
+                          primary_hue="red",
+                          secondary_hue="blue",
+                          neutral_hue="green")
+
+with gr.Blocks(css="""#col_container {margin-left: auto; margin-right: auto;}
+                # DocumentGPT {height: 520px; overflow: auto;} """, theme=theme ) as demo:
+    chatbot = gr.Chatbot(label="DocumentGPTBot")
+    msg = gr.Textbox(label = "DocGPT")
+    clear = gr.ClearButton([msg, chatbot])
+
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history):
+        bot_message = generate_answer(history[-1][0])
+        history[-1][1] = ""
+        for character in bot_message:
+            history[-1][1] += character
+            time.sleep(0.05)
+            yield history
+
+    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    clear.click(lambda: None, None, chatbot, queue=False)
+
+    with gr.Row(visible=True) as button_row:
+        upvote_btn = gr.Button(value="👍  Upvote", interactive=True)
+        downvote_btn = gr.Button(value="👎  Downvote", interactive=True)
+
+demo.queue()
+demo.launch(debug=True)