Create app.py

app.py

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+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()