app.py

import gradio as gr
import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer
import faiss
import matplotlib.pyplot as plt
import seaborn as sns
import time
import os

# Sample FAQs (embedded in script for simplicity)
faq_data = pd.DataFrame({
    'question': [
        'How do I reset my password?', 
        'What are your pricing plans?', 
        'How do I contact support?', 
        None,  # Junk data (null)
        'How do I reset my password?'  # Duplicate
    ],
    'answer': [
        'Go to the login page, click "Forgot Password," and follow the email instructions.',
        'We offer Basic ($10/month), Pro ($50/month), and Enterprise (custom).',
        'Email [email protected] or call +1-800-123-4567.',
        None,  # Junk data
        'Duplicate answer.'  # Duplicate
    ]
})

# Data cleanup function
def clean_faqs(df):
    df = df.dropna()  # Remove nulls
    df = df[~df['question'].duplicated()]  # Remove duplicates
    df = df[df['answer'].str.len() > 20]  # Filter short answers
    return df

# Preprocess FAQs
faq_data = clean_faqs(faq_data)

# Initialize RAG components
embedder = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = embedder.encode(faq_data['question'].tolist(), show_progress_bar=False)
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(embeddings.astype(np.float32))

# RAG process
def rag_process(query, k=2):
    if not query.strip() or len(query) < 5:
        return "Invalid query. Please enter a valid question.", [], {}
    
    start_time = time.perf_counter()
    
    # Embed query
    query_embedding = embedder.encode([query], show_progress_bar=False)
    embed_time = time.perf_counter() - start_time
    
    # Retrieve FAQs
    start_time = time.perf_counter()
    distances, indices = index.search(query_embedding.astype(np.float32), k)
    retrieved_faqs = faq_data.iloc[indices[0]][['question', 'answer']].to_dict('records')
    retrieval_time = time.perf_counter() - start_time
    
    # Generate response (rule-based for free tier)
    start_time = time.perf_counter()
    response = retrieved_faqs[0]['answer'] if retrieved_faqs else "Sorry, I couldn't find an answer."
    generation_time = time.perf_counter() - start_time
    
    # Metrics
    metrics = {
        'embed_time': embed_time * 1000,  # ms
        'retrieval_time': retrieval_time * 1000,
        'generation_time': generation_time * 1000,
        'accuracy': 95.0 if retrieved_faqs else 0.0  # Simulated
    }
    
    return response, retrieved_faqs, metrics

# Plot RAG pipeline
def plot_metrics(metrics):
    data = pd.DataFrame({
        'Stage': ['Embedding', 'Retrieval', 'Generation'],
        'Latency (ms)': [metrics['embed_time'], metrics['retrieval_time'], metrics['generation_time']],
        'Accuracy (%)': [100, metrics['accuracy'], metrics['accuracy']]
    })
    
    plt.figure(figsize=(8, 5))
    sns.set_style("whitegrid")
    sns.set_palette("muted")
    
    ax1 = sns.barplot(x='Stage', y='Latency (ms)', data=data, color='skyblue')
    ax1.set_ylabel('Latency (ms)', color='blue')
    ax1.tick_params(axis='y', labelcolor='blue')
    
    ax2 = ax1.twinx()
    sns.lineplot(x='Stage', y='Accuracy (%)', data=data, marker='o', color='red')
    ax2.set_ylabel('Accuracy (%)', color='red')
    ax2.tick_params(axis='y', labelcolor='red')
    
    plt.title('RAG Pipeline: Latency and Accuracy')
    plt.tight_layout()
    plt.savefig('rag_plot.png')
    plt.close()
    return 'rag_plot.png'

# Gradio interface
def chat_interface(query):
    response, retrieved_faqs, metrics = rag_process(query)
    plot_path = plot_metrics(metrics)
    
    faq_text = "\n".join([f"Q: {faq['question']}\nA: {faq['answer']}" for faq in retrieved_faqs])
    cleanup_stats = f"Cleaned FAQs: {len(faq_data)} (removed {5 - len(faq_data)} junk entries)"
    
    return response, faq_text, cleanup_stats, plot_path

# Dark theme CSS
custom_css = """
body { background-color: #2a2a2a; color: #e0e0e0; }
.gr-box { background-color: #3a3a3a; border: 1px solid #4a4a4a; }
.gr-button { background-color: #1e90ff; color: white; }
.gr-button:hover { background-color: #1c86ee; }
"""

with gr.Blocks(css=custom_css) as demo:
    gr.Markdown("# Crescendo CX Bot Demo")
    gr.Markdown("Enter a query to see the bot's response, retrieved FAQs, and data cleanup stats.")
    
    with gr.Row():
        query_input = gr.Textbox(label="Your Query", placeholder="e.g., How do I reset my password?")
        submit_btn = gr.Button("Submit")
    
    response_output = gr.Textbox(label="Bot Response")
    faq_output = gr.Textbox(label="Retrieved FAQs")
    cleanup_output = gr.Textbox(label="Data Cleanup Stats")
    plot_output = gr.Image(label="RAG Pipeline Metrics")
    
    submit_btn.click(
        fn=chat_interface,
        inputs=query_input,
        outputs=[response_output, faq_output, cleanup_output, plot_output]
    )

demo.launch()