first draft

app.py

@@ -1,19 +1,85 @@
 import gradio as gr
 import pandas as pd
+from langchain.agents import create_pandas_dataframe_agent
+from langchain.chat_models import ChatOpenAI
+import re
+import json
 
 
-def process_inputs(text, file):
-    data = pd.read_csv(file.name)
-    return f"{text}\n\n{data.to_string()}"
+
+def process_inputs(open_api_key, open_api_model, description, csv_file):
+    # setup agent
+
+    try:
+        df = pd.read_csv(csv_file.name)
+        df_agent = create_pandas_dataframe_agent(
+            ChatOpenAI(
+                temperature=0, openai_api_key=open_api_key, model=open_api_model
+            ),
+            df,
+            verbose=True,
+        )
+    except Exception as e:
+        return print(e)
+
+    df_agent.agent.llm_chain.prompt.template = (
+        "Context: " + description + df_agent.agent.llm_chain.prompt.template
+    )
+    # Planning
+    history = {}
+    description = df_agent.run("Describe the data")
+    history["description"] = description
+
+    planning = df_agent.run(
+        f"Description: {description} Based on this description and the data itself, what kind of analysis can we perform on the data? Return result in bullet points without the bullets, just new lines"
+    )
+
+    print(planning)
+
+    # remove non-Alphanumeric characters at the beginning of the string
+    tasks = list(
+        filter(
+            bool, [re.sub(r"^[\W_]+", "", str.strip()) for str in planning.splitlines()]
+        )
+    )
+    history["planning"] = tasks
+
+    # Perform tasks
+    f = open("result.txt", "a")
+    result = []
+    for task in tasks:
+        print(f"The task is: {task}")
+        data = df_agent.run(task)
+        result.append(data)
+        f.write(data + "\n")
+
+    f.close()
+    history["result"] = result
+
+    plots = df_agent.run(
+        f"Based on the information given below, create some insightful plots using matplotlib, seaborn or plotly with python_repl_ast. Save these plots in the root directory with a relevant name, and return a dict where keys are the name of the files and the values are the insights dervied from the plot. Information about the data is as follows: {history}"
+    )
+    history["plots"] = plots
+
+    hypothesis = df_agent.run(
+        f"Based on the information, data, and context given, form valid hypothesis for further investigations. Information about the data is as follows: {history}"
+    )
+    history["hypothesis"] = hypothesis
+
+    history_pretty = json.dump(history, csv_file, indent=2)
+
+    return f"{history_pretty}"
 
 
 iface = gr.Interface(
     fn=process_inputs,
     inputs=[
-        gr.inputs.Textbox(lines=2, placeholder="OpenAI API"),
-        gr.Dataframe(row_count = (2, "dynamic"), col_count=(4,"dynamic"), label="Input Data", interactive=1),
+        gr.Textbox(lines=1, label="OpenAI API Key"),
+        gr.Dropdown(["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-4"], label="Model"),
+        gr.Textbox(lines=2, label="Brief description of the data"),
+        gr.File(label="Upload CSV Only"),
     ],
-    outputs=gr.outputs.Textbox(),
+    outputs=gr.Textbox(),
 )
 
 iface.launch()

flagged/Upload CSV Only/titanic40hqspaj.csv

@@ -0,0 +1,892 @@
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flagged/log.csv

@@ -0,0 +1,3 @@
+text,Upload CSV Only,output,flag,username,timestamp
+,,Error reading file,,,2023-07-09 20:21:06.950524
+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,gpt-3.5-turbo-16k,This data is about titanic,/Users/Chris_Pang/Documents/Code_Repository/csv2md/flagged/Upload CSV Only/titanic40hqspaj.csv,Error reading file,,,2023-07-09 20:44:53.594974

requirements.txt

@@ -0,0 +1,8 @@
+gradio
+numpy
+pandas
+langchain
+matplotlib 
+seaborn 
+plotly
+json

result.txt

@@ -0,0 +1,23 @@
+Yes, we can perform survival analysis, demographic analysis, correlation analysis, and predictive modeling on this data.
+Females had a higher survival rate than males on the Titanic.
+Yes, passengers in higher classes had a higher survival rate. The survival rate decreases as the class number increases.
+The survival rate varies by age. Very young passengers (less than 1 year old) had a high survival rate, while the survival rate decreases for older passengers, with a few exceptions.
+The survival rate is highest for passengers with one sibling/spouse aboard (53.59%). It decreases as the number of siblings/spouses increases, with no survivors among those with five or eight siblings/spouses aboard.
+The survival rate is highest for passengers with 3 parents/children aboard (60%) and lowest for those with 4, 5, or 6 parents/children aboard (0%). Passengers with 1 or 2 parents/children aboard also had a relatively high survival rate (55% and 50% respectively), while those with no parents/children aboard had a survival rate of 34%.
+The average fare for passengers who did not survive was approximately $22.12, while the average fare for passengers who survived was approximately $48.40. This suggests that passengers who paid a higher fare were more likely to survive.
+Yes, the embarkation point had an impact on the survival of the passengers. Passengers who embarked from Cherbourg had a higher survival rate compared to those who embarked from Queenstown and Southampton.
+The correlation between age and fare is approximately 0.096, indicating a very weak positive correlation.
+The correlation between passenger class and fare is `correlation`.
+The average age of passengers is approximately 29.7 years, with a standard deviation of approximately 14.5 years. The youngest passenger is less than a year old (0.42 years), and the oldest passenger is 80 years old. The 25th percentile (Q1) is approximately 20.1 years, the median (Q2) is 28 years, and the 75th percentile (Q3) is 38 years. This means that 50% of the passengers are between 20.1 and 38 years old.
+The average fare among the passengers is approximately 32.20, with a standard deviation of approximately 49.69. The minimum fare is 0, and the maximum fare is 512.33. The 25th percentile is 7.91, the median (50th percentile) is 14.45, and the 75th percentile is 31.00. This suggests that the distribution of fares is skewed to the right, with a few passengers paying much higher fares than most.
+There were 491 passengers in class 3, 216 passengers in class 1, and 184 passengers in class 2.
+There were 577 male and 314 female passengers on the Titanic.
+The distribution of the number of siblings/spouses aboard among the passengers is as follows: 
+- 0 siblings/spouses: 608 passengers
+- 1 sibling/spouse: 209 passengers
+- 2 siblings/spouses: 28 passengers
+- 3 siblings/spouses: 16 passengers
+- 4 siblings/spouses: 18 passengers
+- 5 siblings/spouses: 5 passengers
+- 8 siblings/spouses: 7 passengers
+The distribution of the number of parents/children aboard among the passengers is as follows: 678 passengers had no parents/children aboard, 118 passengers had one parent/child aboard, 80 passengers had two parents/children aboard, 5 passengers had three parents/children aboard, 4 passengers had four parents/children aboard, 5 passengers had five parents/children aboard, and 1 passenger had six parents/children aboard.