Update app.py
Browse files
app.py
CHANGED
@@ -87,14 +87,14 @@ except Exception as e:
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# RAG process
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def rag_process(query, k=2):
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if not query.strip() or len(query) < 5:
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return "Invalid query. Please select a question.",
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start_time = time.perf_counter()
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try:
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query_embedding = embedder.encode([query], show_progress_bar=False)
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embed_time = time.perf_counter() - start_time
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except Exception as e:
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return f"Error embedding query: {str(e)}",
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start_time = time.perf_counter()
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distances, indices = index.search(query_embedding.astype(np.float32), k)
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@@ -141,7 +141,7 @@ def plot_metrics(metrics):
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plt.close()
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return 'rag_plot.png'
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# Gradio interface with
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def chat_interface(query):
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try:
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response, retrieved_faqs, metrics = rag_process(query)
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@@ -167,11 +167,6 @@ body {
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background: linear-gradient(135deg, #1a1a1a 0%, #2a2a2a 100%);
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color: #e0e0e0;
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font-family: 'Arial', sans-serif;
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display: flex;
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justify-content: center;
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align-items: center;
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min-height: 100vh;
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margin: 0;
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}
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.gr-box {
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background: #3a3a3a;
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@@ -185,12 +180,8 @@ body {
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color: white;
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border-radius: 5px;
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padding: 10px 20px;
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margin: 5px
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width: 80%;
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align-self: center;
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text-align: center;
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transition: background 0.3s ease;
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font-size: 16px;
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}
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.gr-button:hover {
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background: #1c86ee;
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@@ -201,43 +192,22 @@ body {
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color: #e0e0e0;
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border: 1px solid #4a4a4a;
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border-radius: 5px;
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margin-bottom: 10px;
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font-size: 14px;
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}
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#app-container {
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max-width: 800px;
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width: 100%;
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padding: 20px;
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background: #252525;
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border-radius: 12px;
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box-shadow: 0 4px 8px rgba(0, 0, 0, 0.5);
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}
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#button-container {
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display: flex;
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flex-
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gap: 10px;
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border-radius: 8px;
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margin-bottom: 20px;
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align-items: center;
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width: 66%;
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}
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#output-container {
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background: #303030;
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padding:
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border-radius: 8px;
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margin
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width: 100%;
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}
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.text-center {
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text-align: center;
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margin-bottom: 20px;
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}
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#app-row {
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display: flex;
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gap: 20px;
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justify-content: space-between;
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}
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"""
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@@ -245,31 +215,28 @@ body {
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unique_questions = faq_data['question'].tolist()
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with gr.Blocks(css=custom_css) as demo:
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faq_output = gr.Textbox(label="Retrieved FAQs", elem_id="faq-output")
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cleanup_output = gr.Textbox(label="Data Cleanup Stats", elem_id="cleanup-output")
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plot_output = gr.Image(label="RAG Pipeline Metrics", elem_id="plot-output")
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demo.launch()
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# RAG process
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def rag_process(query, k=2):
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if not query.strip() or len(query) < 5:
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return "Invalid query. Please select a question.", [], {}
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start_time = time.perf_counter()
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try:
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query_embedding = embedder.encode([query], show_progress_bar=False)
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embed_time = time.perf_counter() - start_time
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except Exception as e:
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return f"Error embedding query: {str(e)}", [], {}
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start_time = time.perf_counter()
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distances, indices = index.search(query_embedding.astype(np.float32), k)
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plt.close()
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return 'rag_plot.png'
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# Gradio interface with buttons and single output panel
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def chat_interface(query):
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try:
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response, retrieved_faqs, metrics = rag_process(query)
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background: linear-gradient(135deg, #1a1a1a 0%, #2a2a2a 100%);
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color: #e0e0e0;
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font-family: 'Arial', sans-serif;
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}
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.gr-box {
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background: #3a3a3a;
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color: white;
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border-radius: 5px;
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padding: 10px 20px;
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margin: 5px;
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transition: background 0.3s ease;
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}
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.gr-button:hover {
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background: #1c86ee;
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color: #e0e0e0;
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border: 1px solid #4a4a4a;
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border-radius: 5px;
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}
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#button-container {
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display: flex;
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flex-wrap: wrap;
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gap: 10px;
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justify-content: center;
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padding: 20px;
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background: #252525;
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border-radius: 8px;
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margin-bottom: 20px;
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}
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#output-container {
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background: #303030;
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padding: 20px;
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border-radius: 8px;
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margin: 10px 0;
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}
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"""
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unique_questions = faq_data['question'].tolist()
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with gr.Blocks(css=custom_css) as demo:
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gr.Markdown("# Customer Experience Bot Demo", elem_classes="text-center")
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gr.Markdown("Select a question to see the bot's response, retrieved FAQs, and call center data cleanup stats.", elem_classes="text-center")
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# Button container for questions
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with gr.Row(elem_id="button-container"):
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for question in unique_questions:
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gr.Button(question).click(
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fn=chat_interface,
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inputs=gr.State(value=question),
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outputs=[
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gr.Textbox(label="Bot Response"),
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gr.Textbox(label="Retrieved FAQs"),
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gr.Textbox(label="Data Cleanup Stats"),
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gr.Image(label="RAG Pipeline Metrics")
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]
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)
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# Single output panel
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with gr.Column(elem_id="output-container"):
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response_output = gr.Textbox(label="Bot Response")
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faq_output = gr.Textbox(label="Retrieved FAQs")
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cleanup_output = gr.Textbox(label="Data Cleanup Stats")
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plot_output = gr.Image(label="RAG Pipeline Metrics")
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demo.launch()
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