Add application file

llm_qa_bot.py

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+# -*- coding: utf-8 -*-
+"""LLM-QA-BOT.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1nx-Eaf7yni5D_8u2SkfAdYg_zcXwOhBA
+"""
+
+!nvidia-smi
+
+! pip install -q langchain gpt-index llama-index transformers sentence_transformers
+
+from llama_index import SimpleDirectoryReader, LangchainEmbedding, GPTListIndex,GPTSimpleVectorIndex, PromptHelper
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from llama_index import LLMPredictor
+import torch
+from langchain.llms.base import LLM
+from transformers import pipeline
+
+class FlanLLM(LLM):
+    model_name = "google/flan-t5-base"
+    pipeline = pipeline("text2text-generation", model=model_name, device=0, model_kwargs={"torch_dtype":torch.bfloat16})
+
+    def _call(self, prompt, stop=None):
+        return self.pipeline(prompt, max_length=9999)[0]["generated_text"]
+ 
+    def _identifying_params(self):
+        return {"name_of_model": self.model_name}
+
+    def _llm_type(self):
+        return "custom"
+
+
+llm_predictor = LLMPredictor(llm=FlanLLM())
+
+hfemb = HuggingFaceEmbeddings()
+embed_model = LangchainEmbedding(hfemb)
+
+text1 = """Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand.
+
+Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving and whether there is something wrong in an image.
+
+Computer vision trains machines to perform these functions, but it has to do it in much less time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex. Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.
+
+Computer vision is used in industries ranging from energy and utilities to manufacturing and automotive – and the market is continuing to grow. It is expected to reach USD 48.6 billion by 2022.1"""
+
+#documents = SimpleDirectoryReader('data').load_data()
+
+from llama_index import Document
+
+text_list = [text1]
+
+documents = [Document(t) for t in text_list]
+
+# set number of output tokens
+num_output = 250
+# set maximum input size
+max_input_size = 512
+# set maximum chunk overlap
+max_chunk_overlap = 20
+
+
+prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)
+
+#index = GPTSimpleVectorIndex(documents, embed_model=embed_model, llm_predictor=llm_predictor)
+
+index = GPTListIndex(documents, embed_model=embed_model, llm_predictor=llm_predictor)
+
+#index.save_to_disk('index.json')
+
+import logging
+
+logging.getLogger().setLevel(logging.CRITICAL)
+
+# response = index.query( "What is computer vision?") 
+response = index.query("It is expected to reach USD 48.6 billion by")
+print(response)
+
+#Frontend
+
+!pip install -q gradio
+
+import gradio as gr
+index = None
+
+def build_the_bot(input_text):
+  text_list = [input_text]
+  documents = [Document(t) for t in text_list]
+  global index
+  index = GPTSimpleVectorIndex(documents, embed_model=embed_model, llm_predictor=llm_predictor)
+  return('Index saved successfull!!!')
+
+def chat(chat_history, user_input):
+  
+  bot_response = index.query(user_input)
+  #print(bot_response)
+  response = ""
+  for letter in ''.join(bot_response.response): #[bot_response[i:i+1] for i in range(0, len(bot_response), 1)]:
+      response += letter + ""
+      yield chat_history + [(user_input, response)]
+
+with gr.Blocks() as demo:
+    gr.Markdown('# Q&A Bot with Hugging Face Models')
+    with gr.Tab("Input Text Document"):
+        text_input = gr.Textbox()
+        text_output = gr.Textbox("Start Building the Bot")
+        text_button = gr.Button("Build the Bot!!!")
+        text_button.click(build_the_bot, text_input, text_output)
+    with gr.Tab("Knowledge Bot"):
+#          inputbox = gr.Textbox("Input your text to build a Q&A Bot here.....")
+          chatbot = gr.Chatbot()
+          message = gr.Textbox ("What is this document about?")
+          message.submit(chat, [chatbot, message], chatbot)
+
+demo.queue().launch(debug = True)