Added Streamlit Dashboard

app.py

@@ -1,33 +1,68 @@
 import streamlit as st
+import streamlit.components.v1 as components
 import openai
 import subprocess
 import re
 import os
 import pandas as pd
-from PIL import Image
+import socket
+import time
 
-df = pd.read_csv("./Financial_Sample.csv")
-metadata = {
-    "columns": df.columns.tolist(),
-    "dtypes": df.dtypes.apply(lambda x: x.name).to_dict(),
-    "shape": df.shape,
-}
+csv_name = "./Financial_Sample.csv"
+try:
+    df = pd.read_csv(csv_name)
+    metadata = {
+        "columns": df.columns.tolist(),
+        "dtypes": df.dtypes.apply(lambda x: x.name).to_dict(),
+        "shape": df.shape,
+    }
+except Exception as e:
+    st.error(f"Error loading CSV file: {e}")
+    st.stop()
 
-def runcode(code: str) -> str:
+
+def is_port_in_use(port: int) -> bool:
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        return s.connect_ex(("localhost", port)) == 0
+
+
+def find_free_port(starting_port: int) -> int:
+    port = starting_port
+    while is_port_in_use(port):
+        port += 1
+    return port
+
+
+def runcode(code: str) -> tuple:
     with open("code.py", "w") as file:
         file.write(code)
 
+    starting_port = 8501
+    free_port = find_free_port(starting_port)
+
     try:
-        result = subprocess.run(
-            ["python", "code.py"], capture_output=True, text=True, check=True
+        process = subprocess.Popen(
+            ["streamlit", "run", "code.py", "--server.port", str(free_port)],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=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
+        time.sleep(5)
+
+        if process.poll() is None:
+            return (
+                free_port,
+                f"Streamlit process started successfully on port {free_port}",
+            )
+        else:
+            stdout, stderr = process.communicate()
+            return (None, f"Streamlit process exited unexpectedly. Error: {stderr}")
+
+    except FileNotFoundError:
+        return (None, "Error: Streamlit is not installed or not in PATH")
+    except Exception as e:
+        return (None, f"An unexpected error occurred: {str(e)}")
 
 
 class Agent:
@@ -37,7 +72,7 @@ class Agent:
         self.known_actions = known_actions if known_actions is not None else {}
 
         self.client = openai.OpenAI(
-            api_key=os.environ.get('TOGETHER_API_KEY'),
+            api_key= os.environ.get('TOGETHER_API_KEY'),
             base_url="https://api.together.xyz/v1",
         )
         self.messages.append({"role": "system", "content": self.system})
@@ -66,6 +101,7 @@ class Agent:
             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)
@@ -73,8 +109,11 @@ class Agent:
                 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}")
+                    error_msg = f"Unknown action: {action}: {action_input}"
+                    raise Exception(error_msg)
+
                 st.session_state.logs += (
                     f" ------------------------\n running {action} {action_input}\n"
                 )
@@ -82,23 +121,21 @@ class Agent:
                 st.session_state.logs += f"Observation: {observation}\n"
                 next_prompt = f"Observation: {observation}"
             else:
+                print("No action found, ending query")
                 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.
+You are an expert in creating advanced Interactive Streamlit Dashboards in python based on user Query 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.
 
-
-for Graphing library use Plotly and save the plot as graph.html
-Always save generated plot as 'graph.html'
 Always return just code no need of ```
 
-Your Task is help user get result of query about below dataset,Decide based on user query to make Plot or Just Textual Answer.
+Your Task is help user get result of query about below dataset,Decide based on user query to make Dashboard or Just Textual Answer.
 Here is the metadata of the dataset and name of dataset is Financial_Sample.csv:
 
 Columns: {metadata['columns']}
@@ -115,64 +152,87 @@ Action : action_name: input_to_action
 if input_to_action is code then don't use ``` just write code
 Example session:
 
-Question: Get the average age of the customers
+Question:  Give me a dashboard to visualize the people height and age
+
+Thought: I need to run a create a dashboard where i can visualize the Subscriptions and Country data
+
+Action: runcode: import streamlit as st
+import pandas as pd
+import plotly.express as px
+from datetime import datetime
 
-Thought: I need to run a query to get the average age of the customers
+@st.cache_data
+def load_data():
+    df = pd.read_csv(f{csv_name})
+    return df
 
-Action: runcode: import pandas as pd
-df=pd.read_csv('./Financial_Sample.csv')
-age = df['age'].mean()
-print(age)
+df = load_data()
+st.title("Height vs Age Visualization")
+fig = px.scatter(df, x='age', y='height', opacity=0.6)
+fig.update_layout(
+    xaxis_title="Age (years)",
+    yaxis_title="Height (cm)",
+    title="Height vs Age Scatter Plot"
+)
+
+st.plotly_chart(fig, use_container_width=True)
 
 PAUSE
 
 Observation :  understand the output based its stdout and take necessary steps. 
 
-Answer: Final Answer for User Request if its graph send "Graph Generated and its Graph Interpretation" or Textual Answer "Your Interpretation of Answer"
+Answer: Final Answer for User Request if its Dashboard send "Please visit link to view dashboard"or Textual Answer "Your Interpretation of Answer"
 """.strip()
 
-
-bot = Agent(system_prompt=prompt, known_actions=known_actions)
+if "bot" not in st.session_state:
+    st.session_state.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)
+if "logs" not in st.session_state:
+    st.session_state.logs = ""
 
-st.sidebar.title("Dataset Metadata")
-st.sidebar.write("Columns:", metadata["columns"])
-st.sidebar.write("Dtypes:", metadata["dtypes"])
-st.sidebar.write("Shape:", metadata["shape"])
-st.sidebar.write("Sample Data:")
-st.sidebar.write(df.head())
+if "messages" not in st.session_state:
+    st.session_state.messages = []
 
-col1, col2 = st.columns(2)
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
 
-if "logs" not in st.session_state:
+if query := st.chat_input("Enter your query about the dataset"):
+    st.session_state.messages.append({"role": "user", "content": query})
+    with st.chat_message("user"):
+        st.markdown(query)
     st.session_state.logs = ""
+    with st.spinner("Generating response..."):
+        st.session_state.bot.query(query)
 
-with col1:
-    if st.button("Submit"):
-        if not query:
-            st.error("Please enter a query.")
-        else:
-            if "graph.html" in os.listdir():
-                os.remove("graph.html")
-            st.session_state.logs = ""
-            with st.spinner("Generating response..."):
-                bot.query(query)
-            answer_match = re.search(r"Answer:", st.session_state.logs)
-            if answer_match:
-                answer_end_index = answer_match.end()
-                subsequent_text = st.session_state.logs[answer_end_index:].strip()
-            st.success(subsequent_text)
+    answer_match = re.search(r"Answer:", st.session_state.logs)
 
-with col2:
-    st.header("Output")
-    if "graph.html" in os.listdir():
-        st.components.v1.html(open("graph.html", "r").read(), height=600)
+    if answer_match:
+        answer_end_index = answer_match.end()
+        subsequent_text = st.session_state.logs[answer_end_index:].strip()
     else:
-        st.write("No plot generated yet.")
-
-with st.expander("Logs"):
-    st.code(st.session_state.logs)
+        st.warning("No answer found in the generated response.")
+
+    with st.chat_message("assistant"):
+        url_pattern = r"(https?://[^\s]+)"
+        url = re.findall(url_pattern, subsequent_text)
+        if url:
+            components.iframe(src=url[0], width=800, height=600)
+            st.write(subsequent_text)
+        else:
+            st.success(subsequent_text)
+    st.session_state.messages.append({"role": "assistant", "content": subsequent_text})
+
+with st.sidebar:
+    st.title("Dataset Metadata")
+    st.write("Columns:", metadata["columns"])
+    st.write("Dtypes:", metadata["dtypes"])
+    st.write("Shape:", metadata["shape"])
+    st.write("Sample Data:")
+    st.write(df.head())
+
+    with st.expander("Logs"):
+        st.code(st.session_state.logs)