Create app.py

app.py

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+import gradio as gr
+import os
+import io
+import base64
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from langchain_openai import ChatOpenAI
+from langchain_experimental.agents.agent_toolkits import create_csv_agent
+from langchain.agents.agent_types import AgentType
+
+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 upload a CSV file and enter your OpenAI API key."
+
+        self.file_path = file.name
+        os.environ["OPENAI_API_KEY"] = api_key
+
+        try:
+            model = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
+            self.agent = create_csv_agent(
+                llm=model,
+                path=self.file_path,
+                verbose=True,
+                agent_type=AgentType.OPENAI_FUNCTIONS,
+                allow_dangerous_code=True
+            )
+            return "CSV file loaded and agent initialized successfully!"
+        except Exception as e:
+            return f"An error occurred: {str(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"![plot](data:image/png;base64,{img_str})"
+                plt.close(fig)  # Close the figure to free up memory
+                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 with OpenAI")
+
+    with gr.Row():
+        file_input = gr.File(label="Upload CSV File")
+        api_key_input = gr.Textbox(label="Enter OpenAI 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()