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Create chat_bot.py
Browse files- chat_bot.py +109 -0
chat_bot.py
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from openai import OpenAI
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import gradio as gr
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import requests
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from datetime import date
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from test_web_rag import get_docs_from_web
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import json
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import os
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from dotenv import load_dotenv
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load_dotenv()
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# Replace with your key
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client = OpenAI()
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you_key = os.getenv("YOU_API_KEY")
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def get_ai_snippets_for_query(query):
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headers = {"X-API-Key": you_key}
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params = {"query": query}
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return requests.get(
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f"https://api.ydc-index.io/search?query={query}",
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params=params,
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headers=headers,
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).json().get('hits')
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def get_web_search_you(query):
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docs = get_ai_snippets_for_query(query)
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markdown = ""
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for doc in docs:
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for key, value in doc.items():
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if key == 'snippets':
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markdown += f"{key}:\n"
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for snippet in value:
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markdown += f"- {snippet}\n"
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else:
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markdown += f"{key}: {value}\n"
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markdown += "\n"
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return markdown
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def predict(message, history, _n_web_search, _strategy):
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# docs = get_web_search_you(message)
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with open('history.json', mode='a', encoding='utf-8') as f:
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json.dump(history, f)
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docs = get_docs_from_web(message, history[-1:], _n_web_search, _strategy)
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partial_message = ''
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information = ''
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for doc in docs:
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if isinstance(doc, dict):
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information = doc.get('data')
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else:
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partial_message = partial_message + doc
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yield partial_message
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system_prompt = """
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You are an advanced chatbot.
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Today's date - {date}
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When answering a question, adhere to the following revised rules:
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- The "Information for reference" data is provided in the chunks with each chunk having its own source as url.
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- Generate human-like text in response to input, reflecting your status as a sophisticated language model.
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- Abstain from offering any health or medical advice and ensure all responses maintain this guideline strictly.
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- Format all responses in markdown format consistently throughout interactions.
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- Must cite sources from the information at the conclusion of your response using properly titled references, but only if the information you provided comes from sources that can be cited.
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Information for reference:
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"{context}"
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Your answer should be structured in markdown as follows:
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<Answer>
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**Sources**:
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Include this section only if the provided information contains sources. If sources are included, list them as follows:
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- [Title of Source 1](URL to Source 1)
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- [Title of Source 2](URL to Source 2)
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... as needed. If no sources are provided, do not include this section in answer.
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""".format(context=information, question=message, date=date.today().strftime('%B %d, %Y'))
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history_openai_format = [{"role": "system", "content": system_prompt}]
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for human, assistant in history:
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history_openai_format.append({"role": "user", "content": human})
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history_openai_format.append({"role": "assistant", "content": assistant})
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history_openai_format.append({"role": "user", "content": message})
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# print(history_openai_format)
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response = client.chat.completions.create(model='gpt-4-turbo',
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messages=history_openai_format,
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temperature=0.5,
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max_tokens=1000,
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top_p=0.5,
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stream=True)
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partial_message += '\n\n'
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for chunk in response:
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if chunk.choices[0].delta.content is not None:
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partial_message = partial_message + chunk.choices[0].delta.content
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yield partial_message
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n_web_search = gr.Slider(1, 10, value=3, step=1, label="Web searches",
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info="Choose between 1 and 10 number of web searches to do. Remember more the web searches more it will take time to reply.")
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strategy = gr.Radio(["Deep", "Normal"], label="Strategy", value="Normal",
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info="Select web search analysis type. Please keep in mind that deep analysis will take more time than normal analysis.")
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app = gr.ChatInterface(predict, additional_inputs=[n_web_search, strategy])
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app.queue(default_concurrency_limit=5)
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app.launch(debug=True, share=False)
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