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| import gradio as gr | |
| import os | |
| import io | |
| import base64 | |
| import matplotlib.pyplot as plt | |
| from langchain_openai import ChatOpenAI | |
| from langchain_experimental.agents.agent_toolkits import create_csv_agent | |
| from langchain.agents.agent_types import AgentType | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| class CSVChatBot: | |
| def __init__(self): | |
| self.agent = None | |
| self.file_path = None | |
| def initialize_agent(self, file, api_key): | |
| if file is None or api_key.strip() == "": | |
| return "⚠ Please Enter your API key and Upload the CSV Data/File" | |
| self.file_path = file.name | |
| try: | |
| model = ChatOpenAI(temperature=0.5, model="mistralai/mistral-7b-instruct:free",base_url="https://openrouter.ai/api/v1",api_key=os.getenv("OPENAI_API_KEY")) | |
| self.agent = create_csv_agent( | |
| llm=model, | |
| path=self.file_path, | |
| agent_type=AgentType.OPENAI_FUNCTIONS, | |
| allow_dangerous_code=True, | |
| agent_executor_kwargs=dict(handle_parsing_errors=True) | |
| ) | |
| return "🟢 Congratulations!! CSV FIle Uploded and Agent is Initialized Successfully" | |
| except Exception as e: | |
| return f"Oops!! Error Occured because of: {e}" | |
| def chat(self, message, history): | |
| if self.agent is None: | |
| return "Please initialize the agent first by uploading a CSV file and providing an API key." | |
| try: | |
| CSV_PROMPT_PREFIX = "First get the column names from the CSV file, then answer the question." | |
| CSV_PROMPT_SUFFIX = """ | |
| - **ALWAYS** before giving the Final Answer, try another method. | |
| Then reflect on the answers of the two methods you did and ask yourself | |
| if it answers correctly the original question. | |
| If you are not sure, try another method. | |
| - If the methods tried do not give the same result, reflect and | |
| try again until you have two methods that have the same result. | |
| - If you still cannot arrive to a consistent result, say that | |
| you are not sure of the answer. | |
| - If you are sure of the correct answer, create a beautiful | |
| and thorough response using Markdown. | |
| - **DO NOT MAKE UP AN ANSWER OR USE PRIOR KNOWLEDGE, | |
| ONLY USE THE RESULTS OF THE CALCULATIONS YOU HAVE DONE**. | |
| - **ALWAYS**, as part of your "Final Answer", explain how you got | |
| to the answer on a section that starts with: "\n\nExplanation:\n". | |
| In the explanation, mention the column names that you used to get | |
| to the final answer. | |
| """ | |
| result = self.agent.run(CSV_PROMPT_PREFIX + message + CSV_PROMPT_SUFFIX) | |
| fig = plt.gcf() | |
| if fig.get_axes(): | |
| buf = io.BytesIO() | |
| fig.savefig(buf, format='png') | |
| buf.seek(0) | |
| img_str = base64.b64encode(buf.getvalue()).decode() | |
| img_markdown = f"" | |
| plt.clf() | |
| plt.close(fig) | |
| return result + "\n\n" + img_markdown | |
| else: | |
| return result | |
| except Exception as e: | |
| return f"An error occurred: {str(e)}" | |
| csv_chatbot = CSVChatBot() | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# CSV Analysis Chatbot") | |
| gr.Markdown("#### Made by Samagra Shrivastava with ♥") | |
| with gr.Row(): | |
| file_input = gr.File(label="Upload CSV File/Data") | |
| api_key_input = gr.Textbox(label="Enter OpenRouter API Key", type="password") | |
| initialize_button = gr.Button("Initialize Agent") | |
| init_output = gr.Textbox(label="Initialization Status") | |
| chatbot = gr.Chatbot() | |
| msg = gr.Textbox() | |
| clear = gr.Button("Clear") | |
| def user(user_message, history): | |
| return "", history + [[user_message, None]] | |
| def bot(history): | |
| user_message = history[-1][0] | |
| bot_message = csv_chatbot.chat(user_message, history) | |
| history[-1][1] = bot_message | |
| return history | |
| def clear_chat(): | |
| return None | |
| initialize_button.click(csv_chatbot.initialize_agent, inputs=[file_input, api_key_input], outputs=init_output) | |
| msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then( | |
| bot, chatbot, chatbot | |
| ) | |
| clear.click(clear_chat, None, chatbot, queue=False) | |
| demo.launch() |