app

app.py

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+import chainlit as cl
+import pandas as pd
+import io
+import matplotlib.pyplot as plt
+import base64
+from io import BytesIO
+from pandasai import SmartDataframe
+import pandas as pd
+from pandasai.llm import OpenAI
+from io import StringIO
+import matplotlib.pyplot as plt
+import csv
+from collections import defaultdict
+
+
+def find_most_valuable_feature(csv_file):
+    print("find_most_valuable_feature")
+    print(csv_file)
+
+    openai.api_key = os.environ["OPENAI_API_KEY"]
+    smart_llm = OpenAI()
+
+    
+
+
+    # Initialize a defaultdict to store column data
+    columns = defaultdict(list)
+  
+    # Read the CSV file and populate the defaultdict
+    with open("upload.csv") as f:
+        reader = csv.reader(f)
+        headers = next(reader)
+
+        for row in reader:
+            for header, value in zip(headers, row):
+                columns[header].append(value)
+
+    # Manually create a DataFrame from the defaultdict
+    smart_df = pd.DataFrame({
+        "ID": columns["ID"],
+        "Date and Time": columns["Date and Time"],
+        "Business Unit": columns["Business Unit"],
+        "Usage Change": columns["Usage Change"],
+        "Wolftech Improvement": columns["Wolftech Improvement"],
+        "Likelihood to Recommend": columns["Likelihood to Recommend"],
+        "Effective Training": columns["Effective Training"],
+        "Most Valuable Feature": columns["Most Valuable Feature"]
+    })
+
+    smart_df = SmartDataframe(smart_df, config={"llm": smart_llm})
+    out = smart_df.chat('Summarize the top three "Most Valuable Feature" for people where Usage Changed was Increased?')
+
+    print(out)
+
+
+    df = out
+
+    # Plotting
+    plt.figure(figsize=(10, 6))
+    plt.bar(df["Most Valuable Feature"], df["Count"], color='blue')
+    plt.xlabel('Most Valuable Feature')
+    plt.ylabel('Count')
+    plt.title('Count of Most Valuable Features')
+    plt.xticks(rotation=45, ha="right")  # Rotate labels for better readability
+    plt.tight_layout()  # Adjust layout for better fit
+
+    # Save the plot to a BytesIO object
+    image_buffer = BytesIO()
+    plt.savefig(image_buffer, format='png')
+    image_buffer.seek(0)
+
+    return image_buffer
+
+
+
+
+
+def process_and_analyze_data(csv_file):
+    # Read CSV file
+    csv_data = pd.read_csv(csv_file)
+
+    # Logging to check data loading
+    print(f"CSV Data Loaded: {csv_data.head()}")
+
+    # Count of responses in each category of 'Business Unit'
+    business_unit_counts = csv_data['Business Unit'].value_counts()
+
+    # Plotting the count of responses in each 'Business Unit' category
+    plt.figure(figsize=(10, 6))
+    business_unit_counts.plot(kind='bar')
+    plt.title('Count of Responses by Business Unit')
+    plt.xlabel('Business Unit')
+    plt.ylabel('Count')
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+
+   # Save the plot to a BytesIO object
+    image_buffer = BytesIO()
+    plt.savefig(image_buffer, format='png')
+    image_buffer.seek(0)
+
+    return image_buffer
+
+
+# Function to handle message events
+
+@cl.on_message
+async def handle_message(message: cl.Message):
+    # Retrieve the CSV file from the message
+    csv_file = next(
+        (
+            io.BytesIO(file.content)
+            for file in message.elements or []
+            if file.mime and "csv" in file.mime
+        ),
+        None,
+    )
+
+    # Logging to check file retrieval
+    print(f"CSV File: {csv_file}")
+
+    if csv_file:
+        try:
+            
+            image_buffer = find_most_valuable_feature(csv_file)
+
+            # Get bytes data from BytesIO object and send the image data
+            image_data = image_buffer.getvalue()
+            name = "chart"
+            cl.user_session.set(name, image_data)
+            cl.user_session.set("generated_image", name)
+           
+            await cl.Message(content="Based on the people who increased usage, here are the most valuable features").send()
+
+            generated_image = cl.user_session.get(name)
+
+            elements = []
+            actions = []
+
+            elements = [
+                cl.Image(
+                    content=generated_image,
+                    name=name,
+                    display="inline",
+                    size="large"
+                )
+            ]
+
+            await cl.Message(content=name, elements=elements, actions=actions).send()
+
+
+        except Exception as e:
+            await cl.Message(content=f"An error occurred: {str(e)}").send()
+    else:
+        await cl.Message(content="Please upload a CSV file.").send()
+
+
+# Run the ChainLit app
+if __name__ == "__main__":
+    cl.run()