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app.py

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+import streamlit as st
+import openai
+import subprocess
+import re
+import os
+import pandas as pd
+from PIL import Image
+
+df = pd.read_csv("./customers-10000.csv")
+metadata = {
+    "columns": df.columns.tolist(),
+    "dtypes": df.dtypes.apply(lambda x: x.name).to_dict(),
+    "shape": df.shape,
+}
+
+def runcode(code: str) -> str:
+    with open("code.py", "w") as file:
+        file.write(code)
+
+    try:
+        result = subprocess.run(
+            ["python", "code.py"], capture_output=True, text=True, check=True
+        )
+        output = "Output Generated Successfully" + "\n" + result.stdout
+    except subprocess.CalledProcessError as e:
+        output = f"An error occurred while running the code:\nSTDOUT:\n{e.stdout}\nSTDERR:\n{e.stderr}"
+    except Exception as e:
+        output = f"An unexpected error occurred: {str(e)}"
+
+    return output
+
+
+class Agent:
+    def __init__(self, system_prompt="", known_actions=None):
+        self.system = system_prompt
+        self.messages = []
+        self.known_actions = known_actions if known_actions is not None else {}
+
+        self.client = openai.OpenAI(
+            api_key=os.environ.get('TOGETHER_API_KEY'),
+            base_url="https://api.together.xyz/v1",
+        )
+        self.messages.append({"role": "system", "content": self.system})
+
+    def __call__(self, message):
+        self.messages.append({"role": "user", "content": message})
+        result = self.execute()
+        self.messages.append({"role": "assistant", "content": result})
+        return result
+
+    def execute(self):
+        try:
+            response = self.client.chat.completions.create(
+                model="meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
+                stop=["PAUSE"],
+                messages=self.messages,
+            )
+            return response.choices[0].message.content
+        except Exception as e:
+            return f"Error executing model: {str(e)}"
+
+    def query(self, question, max_turns=5):
+        i = 0
+        next_prompt = question
+        while i < max_turns:
+            i += 1
+            result = self(next_prompt)
+            st.session_state.logs += f"Assistant: {result}\n"
+            action_re = re.search(r"Action: (\w+): (.*)", result, re.DOTALL)
+            if action_re:
+                action = action_re.group(1)
+                action_input = action_re.group(2).strip()
+                st.session_state.logs += (
+                    f"Action: {action}\nAction Input: {action_input}\n"
+                )
+                if action not in self.known_actions:
+                    raise Exception(f"Unknown action: {action}: {action_input}")
+                st.session_state.logs += (
+                    f" ------------------------\n running {action} {action_input}\n"
+                )
+                observation = self.known_actions[action](action_input)
+                st.session_state.logs += f"Observation: {observation}\n"
+                next_prompt = f"Observation: {observation}"
+            else:
+                return
+
+
+known_actions = {"runcode": runcode}
+
+prompt = f"""
+You are an expert in writing python code based on user request and you run in a loop of Thought, Action, PAUSE, Observation.
+At the end of the loop you output an Answer
+Use Thought to describe your thoughts about the question you have been asked.
+Use Action to run one of the actions available to you - then return PAUSE.
+Observation will be the result of running those actions.
+Always return just code no need of ```
+Always save generated plot as 'graph.png'
+
+Your Task is help user get result of query about below dataset,Decide based on user query to make Plot or Just Textual Answer.
+Here is the metadata of the dataset and name of dataset is customers-10000.csv:
+
+Columns: {metadata['columns']}
+Dtypes: {metadata['dtypes']}
+Shape: {metadata['shape']}
+You can use this metadata to generate results.
+
+Your available actions are:
+runcode
+
+How to use actions
+Action : action_name: input_to_action
+
+if want to use matplotlib use backend as Agg
+
+Example session:
+
+Question: Get the average age of the customers
+
+Thought: I need to run a query to get the average age of the customers
+
+Action: runcode: import pandas as pd
+df=pd.read_csv('./customers-10000.csv')
+age = df['age'].mean()
+print(age)
+
+PAUSE
+
+Observation :  understand the output based its stdout and take necessary steps. 
+
+Answer: Final Answer for User Request if its graph send "Graph Generated" or Textual Answer "Your Interpretation of Answer"
+""".strip()
+
+bot = Agent(system_prompt=prompt, known_actions=known_actions)
+
+st.set_page_config(layout="wide")
+st.title("Customer Data Analysis")
+
+query = st.text_area("Enter your query about the dataset:", height=100)
+
+col1, col2 = st.columns(2)
+
+if "logs" not in st.session_state:
+    st.session_state.logs = ""
+
+with col1:
+    if st.button("Submit"):
+        if "graph.png" in os.listdir():
+            os.remove("graph.png")
+        st.session_state.logs = ""
+        with st.spinner("Generating response..."):
+            bot.query(query)
+        answer = re.findall(r"Answer: (.+)", st.session_state.logs)[-1]
+        st.success(answer)
+
+with col2:
+    st.header("Output")
+    if "graph.png" in os.listdir():
+        image = Image.open("graph.png")
+        st.image(image, caption="Generated Plot")
+    else:
+        st.write("No plot generated yet.")
+
+st.header("Live Logs")
+st.text_area("Logs", value=st.session_state.logs, height=400)

requirements.txt

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+streamlit
+openai
+pillow