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Update app.py
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app.py
CHANGED
@@ -13,6 +13,7 @@ from kadiApy_ragchain import KadiApyRagchain
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load_dotenv()
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vectorstore_path = "data/vectorstore"
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GROQ_API_KEY = os.environ["GROQ_API_KEY"]
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HF_TOKEN = os.environ["HF_Token"]
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@@ -20,42 +21,47 @@ with open("config.json", "r") as file:
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config = json.load(file)
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login(HF_TOKEN)
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# Access the values
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LLM_MODEL_NAME = config["llm_model_name"]
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LLM_MODEL_TEMPERATURE = float(config["llm_model_temperature"])
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# A class to encapsulate the bot logic
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class KadiBot:
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def __init__(self, hf_token: str, groq_api_key: str, config: dict, vectorstore_path: str):
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self.vectorstore = get_chroma_vectorstore(get_SFR_Code_embedding_model(), vectorstore_path)
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self.llm = get_groq_llm(config["llm_model_name"], float(config["llm_model_temperature"]), groq_api_key)
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self.kadiAPY_ragchain = KadiApyRagchain(self.llm, self.vectorstore)
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def add_text_to_chat_history(chat_history, user_input):
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chat_history = chat_history + [(user_input, None)]
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return chat_history, ""
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def show_history(chat_history):
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return chat_history
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def reset_all():
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return [], "", ""
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def main():
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# Initialize the KadiBot
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kadi_bot = KadiBot(HF_TOKEN, GROQ_API_KEY, config, vectorstore_path)
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with gr.Blocks() as demo:
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gr.Markdown("## KadiAPY - AI Coding-Assistant")
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gr.Markdown("AI assistant for KadiAPY based on RAG architecture powered by LLM")
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@@ -87,12 +93,12 @@ def main():
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cache_examples=False,
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examples_per_page=3,
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)
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# Use the state to persist chat history between interactions
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user_txt.submit(add_text_to_chat_history, [chat_history, user_txt], [chat_history, user_txt]).then(show_history,
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.then(
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submit_btn.click(add_text_to_chat_history, [chat_history, user_txt], [chat_history, user_txt]).then(show_history,
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.then(
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clear_btn.click(
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reset_all,
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None,
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@@ -101,6 +107,5 @@ def main():
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demo.launch()
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if __name__ == "__main__":
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main()
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load_dotenv()
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vectorstore_path = "data/vectorstore"
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GROQ_API_KEY = os.environ["GROQ_API_KEY"]
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HF_TOKEN = os.environ["HF_Token"]
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config = json.load(file)
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login(HF_TOKEN)
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hf_api = HfApi()
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# Access the values
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LLM_MODEL_NAME = config["llm_model_name"]
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LLM_MODEL_TEMPERATURE = float(config["llm_model_temperature"])
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def initialize():
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global kadiAPY_ragchain
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vectorstore = get_chroma_vectorstore(get_SFR_Code_embedding_model(), vectorstore_path)
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llm = get_groq_llm(LLM_MODEL_NAME, LLM_MODEL_TEMPERATURE, GROQ_API_KEY)
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kadiAPY_ragchain = KadiApyRagchain(llm, vectorstore)
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initialize()
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def bot_kadi(chat_history):
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user_query = chat_history[-1][0]
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response = kadiAPY_ragchain.process_query(user_query, chat_history)
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chat_history[-1] = (user_query, response)
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return chat_history
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import gradio as gr
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def add_text_to_chat_history(chat_history, user_input):
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chat_history = chat_history + [(user_input, None)]
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return chat_history, ""
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def show_history(chat_history):
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return chat_history
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def reset_all():
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return [], "", ""
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def main():
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with gr.Blocks() as demo:
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gr.Markdown("## KadiAPY - AI Coding-Assistant")
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gr.Markdown("AI assistant for KadiAPY based on RAG architecture powered by LLM")
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cache_examples=False,
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examples_per_page=3,
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)
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# Use the state to persist chat history between interactions
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user_txt.submit(add_text_to_chat_history, [chat_history, user_txt], [chat_history, user_txt]).then(show_history,[chat_history], [chatbot])\
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.then(bot_kadi, [chat_history], [chatbot])
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submit_btn.click(add_text_to_chat_history, [chat_history, user_txt], [chat_history, user_txt]).then(show_history,[chat_history], [chatbot])\
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.then(bot_kadi, [chat_history], [chatbot])
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clear_btn.click(
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reset_all,
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None,
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)
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demo.launch()
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if __name__ == "__main__":
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main()
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