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import gradio as gr |
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from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper |
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from langchain import OpenAI |
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import sys |
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import os |
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directory_path = r'C:\Users\paddy\Open_AI\Data' |
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os.environ["OPENAI_API_KEY"] = '' |
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def construct_index(): |
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max_input_size = 4096 |
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num_outputs = 256 |
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max_chunk_overlap = 0 |
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chunk_size_limit = 600 |
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prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) |
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llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo", max_tokens=num_outputs)) |
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documents = SimpleDirectoryReader(directory_path).load_data() |
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index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper) |
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index.save_to_disk('index.json') |
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return index |
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def chatbot_response(query_input, chat_history=[]): |
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index = construct_index() |
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response = index.query(query_input, response_mode="compact") |
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chat_history.append(("You:\n", query_input)) |
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chat_history.append(("Bot:", response.response)) |
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chat_history_text = "\n\n".join(["{}{}".format(item[0], item[1]) for item in chat_history[-9:-1]]) |
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response_text = f"{chat_history_text} \n\nBot:{response.response}" |
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return response_text |
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chat_history = [] |
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query_input = gr.Textbox(label="What do you want to ask the bot?") |
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output_text = gr.Textbox(label="Bot Response and Chat History") |
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def get_chatbot_response(query_input): |
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global chat_history |
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response_text = chatbot_response(query_input, chat_history) |
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return response_text |
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interface = gr.Interface( |
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fn=get_chatbot_response, |
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inputs=query_input, |
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outputs=output_text, |
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title="Chatbot", |
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description="Ask the bot any question and it will give you a response.", |
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examples=["What are some popular tourist destinations in Europe?","What are some famous landmarks in Paris?","What can visitors do in Rome, Italy?","What are some must-see sights in Rome?"], |
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) |
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interface.launch() |
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