app.py

from openai import OpenAI
import gradio as gr
import os
import base64
import numpy as np
from PIL import Image
import io
import pandas as pd
from dotenv import load_env

load_env()
api_key = os.getenv("OPENAI_API_KEY")
client = OpenAI(
    # This is the default and can be omitted
    api_key = api_key,
)

def analyze_feedback(feedback_text):
    """Analyzes feedback text and returns a summary.

    Args:
        feedback_text (str): The text of the feedback to analyze.

    Returns:
        str: A summary of the feedback.
    """
    chat_completion = client.chat.completions.create(
    messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": feedback_text}
        ]
    ,
    model="gpt-3.5-turbo"
    )
    return chat_completion.choices[0].message.content

def process_files(uploaded_files):
    results = [["File Name", "Summary", "Areas of Improvement", "Actionable Objectives","Sentiment", "Themes"]]

    for file_path in uploaded_files:
        # Extract file name from the file path
        file_name = os.path.basename(file_path)

        # Open and read the file content
        with open(file_path, 'r', encoding='utf-8') as file:
            feedback_text = file.read()
            #print(feedback_text)

        # Process the feedback text
        processed_feedback = process_feedback(feedback_text)
        
        #print ('processed', processed_feedback)
        # Example result format for each file
        file_result = {
            "summary": processed_feedback['summary'],
            "areas_of_improvement": processed_feedback['areas_of_improvement'],
            "actionable_objectives": processed_feedback['actionable_objectives'],
            "sentiment": processed_feedback['sentiment'],
            "themes": processed_feedback['themes']
        }
        
        #print(file_result)
        #print (type(file_result))

    return feedback_text, file_result


def process_feedback(feedback_text):
    # Example processing steps
    # You need to replace these with your actual feedback analysis logic
    #print (feedback_text)
    summary = analyze_feedback("Provide the summary of the feedback first and then after the summary, analyze and list down the top achievements in this Feedback and pull out any key themes from the Feedback: " + feedback_text)
    
    # Simple sentiment analysis
    sentiment = analyze_feedback("What are the overall sentiment score and reasons for the score in this feedback, if 1 is negative and 10 is rated as positive. Provide responses as colon delimeted format: Who gave the Feedback:What was the sentiment score:why was the score given for this feedback: " + feedback_text)

    # Theme identification (implement your logic)
    themes = analyze_feedback("What are the key recurring positive and negative themes in this feedback: " + feedback_text)
    
    #print ("Summ",summary)
    #strengths = analyze_feedback("What are the key strengths in this feedback: " + feedback_text)
    areas_of_improvement = analyze_feedback("Which hard and soft skills may need development according to this Feedback: " + feedback_text)
    actionable_objectives = analyze_feedback("Suggest actionable objectives based on this feedback and steps to accomplish these objectives: " + feedback_text)

    # This depends on how the response is formatted. You may need to adjust the parsing logic
    sentiment_data = []  # This will be a list of dictionaries
    print (sentiment)
    # Example of processing (you'll need to adjust this based on actual response format)
    for line in sentiment.split('\n'):
        if line.strip():
            parts = line.split(':')
            if len(parts) >= 3:
                sentiment_data.append({
                    "Giver": parts[0].strip(),
                    "Score": parts[1].strip(),
                    "Reason": parts[2].strip()
                })
    # Assign the results to analysis_results
    analysis_results = {
        "summary": summary,
        "themes": themes,
        "areas_of_improvement": areas_of_improvement,
        "actionable_objectives": actionable_objectives,
        "sentiment": sentiment_data  
    }
    #print (analysis_results)

    return analysis_results

def display_results(uploaded_files):
    """
    Processes uploaded feedback files and displays the results.

    Args:
        uploaded_files (list): A list of file paths to uploaded feedback files.

    Returns:
        tuple: A tuple containing various analysis results or an empty string if no files were uploaded.
    """
    feedback, results = process_files(uploaded_files)
    if results:
        # Extract each area of feedback from the dictionary
        summary = results['summary'] if 'summary' in results else ""
        areas_of_improvement = results['areas_of_improvement'] if 'areas_of_improvement' in results else ""
        actionable_objectives = results['actionable_objectives'] if 'actionable_objectives' in results else ""
        themes_text = results['themes'] if 'themes' in results else ""
        #sentiments_text = results['sentiment'] if 'sentiment' in results else ""
        sentiment_table=[]
        if results:
            for sentiment_entry in results['sentiment']:
                entry_as_list = [sentiment_entry['Giver'], sentiment_entry['Score'], sentiment_entry['Reason']]
                sentiment_table.append(entry_as_list)
                
        # Create a Pandas DataFrame
        df = pd.DataFrame(sentiment_table, columns=["Giver", "Score", "Reason"])
        df = df.iloc[1:]
        return summary, themes_text, areas_of_improvement, actionable_objectives,df
    else:
        return "","","","",[], None

def image_to_base64(image_path):
    """
    Reads an image file and returns its base64 encoded representation.

    Args:
        image_path (str): The path to the image file.

    Returns:
        str: The base64 encoded representation of the image data.
    """

    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")


# Encode the logo image into base64
logo_base64 = image_to_base64("pixelpk_logo.png")


markdown_content = f"""
<img src="data:image/png;base64,{logo_base64}" alt="Feedback Logo" style="width: 100px; height: 100px; margin-top: 10px;" />
<p style="margin-top: 5px;">Feedback analyzer give employee/students feedback, providing key insights into strengths, areas for improvement, and overall sentiments.</p>
"""

# Custom CSS for styling the interface
custom_css = """
<style>
  .gradio-container {
    font-family: 'Helvetica Neue', Arial, sans-serif;
  }
  .gradio-accordion {
    border-radius: 5px;
  }
  .gradio-accordion .gradio-tab {
    padding: 10px;
  }
  .gradio-textbox {
    height: auto;
    min-height: 50px;
    max-height: 300px; /* Adjust as needed */
    overflow-y: auto; /* Enables vertical scrolling */
  }
</style>
"""


with gr.Blocks(gr.themes.Monochrome(), css=custom_css) as demo:
    # Display introductory markdown content
    gr.Markdown(f"<center>{markdown_content}</center>")
    # Layout elements in a row
    with gr.Column():

        # Upload button for selecting files
        upload_button = gr.File(file_types=["txt"], file_count="multiple")
        # Button to trigger analysis
        submit_button = gr.Button("Submit")

    # Accordion to display analysis results
    with gr.Accordion("Analysis Results"):
        # Tab for summary information
        with gr.Tab("Summary"):
            output_summary = gr.Textbox(label="Summary", lines = 4, placeholder="Summary will appear here...")
        # Tab for key insights
        with gr.Tab("Key Insights"):
            output_improvement_areas = gr.Textbox(label="Key Insights", lines = 15, placeholder="Themes will appear here...")
        # Tab for specific improvement areas
        with gr.Tab("Improvement Areas"):
            output_themes = gr.Textbox(label="Analysis", lines = 15, placeholder="Analysis will appear here...")
        # Tab for actionable objectives
        with gr.Tab("Objectives"):
            output_actionable_objectives = gr.Textbox(label="Actionable Objectives", lines = 15, placeholder="Objectives will appear here...")
        # Tab for sentiment analysis results
        with gr.Tab("Sentiments"):
            output_sentiment_table = gr.Dataframe(label="Sediment Analysis")

    # Connect button click to analysis function
    submit_button.click(
        display_results,
        inputs = [upload_button],
        outputs = [
            output_summary,
            output_themes,
            output_improvement_areas,
            output_actionable_objectives,
            output_sentiment_table
        ]
    )

    # Launch the Gradio interface
    demo.launch(share = True)