Upload interface.py

interface.py

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+import base64
+import os
+import tempfile
+from io import BytesIO
+
+import gradio as gr
+import pandas as pd
+import requests
+
+
+custom_css = """
+/* 1. Deep Neutral Background */
+.gradio-container {
+    background-color: #111111 !important;
+    font-family: 'Inter', system-ui, sans-serif !important;
+}
+
+/* 2. High-Contrast Headers */
+h1 {
+    font-weight: 800 !important;
+    background: linear-gradient(90deg, #ffffff, #a0aec0);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    font-size: 2.5rem !important;
+    text-align: center;
+    margin-bottom: 2rem !important;
+}
+
+/* 3. Defined Cards with Subtle Borders */
+.gradio-container .block {
+    background-color: #1a1a1a !important;
+    border: 1px solid #2d2d2d !important;
+    border-radius: 16px !important;
+    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.5) !important;
+}
+
+/* 4. Neon-Accent Primary Button */
+button.primary {
+    background: #ffffff !important; /* White button on dark background is very 'chic' */
+    color: #000000 !important;
+    border: none !important;
+    font-weight: 700 !important;
+    text-transform: uppercase;
+    letter-spacing: 1px;
+    border-radius: 12px !important;
+    padding: 14px !important;
+    transition: all 0.3s ease !important;
+}
+
+button.primary:hover {
+    background: #e2e8f0 !important;
+    transform: scale(1.02);
+}
+
+/* 5. Readable Inputs & Labels */
+input, textarea, .gr-dropdown {
+    background-color: #262626 !important;
+    border: 1px solid #3f3f3f !important;
+    color: #ffffff !important;
+    border-radius: 10px !important;
+}
+
+label span {
+    color: #a0aec0 !important; /* Muted silver for labels */
+    font-weight: 500 !important;
+    text-transform: uppercase;
+    font-size: 0.8rem !important;
+}
+
+/* 6. Fix for readable text in Output components */
+.focusable, .selectable {
+    color: #ffffff !important;
+}
+"""
+
+
+API_BASE_URL = os.getenv("MULTIMODAL_API_BASE_URL", "http://127.0.0.1:7861")
+EXTRACTION_API_URL = os.getenv(
+    "MULTIMODAL_API_URL",
+    f"{API_BASE_URL.rstrip('/')}/information_extraction/",
+)
+MAPPING_API_URL = os.getenv(
+    "MULTIMODAL_MAPPING_API_URL",
+    f"{API_BASE_URL.rstrip('/')}/mapping/",
+)
+
+
+def process_zip(zip_file, employee_code, debug=False):
+    if zip_file is None:
+        raise gr.Error("Please upload a ZIP file.")
+    if not employee_code or not employee_code.strip():
+        raise gr.Error("Please enter an employee code.")
+
+    with open(zip_file, "rb") as file_obj:
+        response = requests.post(
+            EXTRACTION_API_URL,
+            files={
+                "file": (
+                    os.path.basename(zip_file),
+                    file_obj,
+                    "application/zip",
+                )
+            },
+            data={
+                "employee_code": employee_code.strip(),
+                "debug": str(debug).lower(),
+            },
+            timeout=300,
+        )
+
+    try:
+        payload = response.json()
+    except ValueError as exc:
+        raise gr.Error("The API returned an invalid response.") from exc
+
+    if response.status_code != 200:
+        error_message = payload.get("detail") or payload.get("message") or "Request failed."
+        raise gr.Error(error_message)
+
+    excel_base64 = payload.get("excel_data_base64")
+    if not excel_base64:
+        raise gr.Error("The API response did not include an Excel file.")
+
+    excel_bytes = base64.b64decode(excel_base64)
+    dataframe = pd.read_excel(BytesIO(excel_bytes))
+
+    output_path = os.path.join(
+        tempfile.gettempdir(),
+        f"extraction_result_{employee_code.strip()}.xlsx",
+    )
+    with open(output_path, "wb") as excel_file:
+        excel_file.write(excel_bytes)
+
+    status = (
+        f"{payload.get('message', 'Processing completed.')} "
+        f"Time: {payload.get('duration', 'N/A')}s."
+    )
+
+    return status, dataframe, output_path
+
+
+def process_mapping(product_list, dense_weight=0.7, sparse_weight=0.3, normalize=True):
+    if not product_list or not product_list.strip():
+        raise gr.Error("Please enter at least one product name.")
+
+    response = requests.post(
+        MAPPING_API_URL,
+        data={
+            "product_list": product_list.strip(),
+            "dense_weight": dense_weight,
+            "sparse_weight": sparse_weight,
+            "normalize": str(normalize).lower(),
+        },
+        timeout=300,
+    )
+
+    try:
+        payload = response.json()
+    except ValueError as exc:
+        raise gr.Error("The API returned an invalid response.") from exc
+
+    if response.status_code != 200:
+        error_message = payload.get("detail") or payload.get("message") or "Request failed."
+        raise gr.Error(error_message)
+
+    results = payload.get("results")
+    if not isinstance(results, list):
+        raise gr.Error("The API response did not include mapping results.")
+
+    dataframe = pd.DataFrame(results)
+    if dataframe.empty:
+        dataframe = pd.DataFrame(
+            columns=["original_product_name", "top_1", "top_2", "top_3", "top_4", "top_5"]
+        )
+    else:
+        preferred_columns = [
+            "original_product_name",
+            "top_1",
+            "top_2",
+            "top_3",
+            "top_4",
+            "top_5",
+        ]
+        dataframe = dataframe.reindex(columns=preferred_columns)
+
+    total_products = payload.get("total_products", len(dataframe))
+    duration = payload.get("api_duration", payload.get("duration", "N/A"))
+    status = f"Mapped {total_products} products. Time: {duration}s."
+
+    return status, dataframe
+
+
+with gr.Blocks(title="Multimodal OCR and Product Mapping") as demo:
+    gr.Markdown(
+        """
+        # Multimodal OCR and Product Mapping Interface
+        ## Run information extraction or product mapping from a single interface.
+        """ 
+    )
+
+    with gr.Tabs():
+        with gr.Tab("Information Extraction"):
+            with gr.Row():
+                zip_input = gr.File(
+                    label="ZIP File",
+                    file_types=[".zip"],
+                    type="filepath",
+                )
+                employee_code_input = gr.Textbox(
+                    label="Employee Code",
+                    placeholder="e.g. admin",
+                )
+
+            debug_input = gr.Checkbox(label="Debug mode", value=False)
+            submit_button = gr.Button("Process", variant="primary")
+
+            status_output = gr.Textbox(label="Status", interactive=False)
+            excel_preview = gr.Dataframe(label="Excel Preview", interactive=False)
+            excel_download = gr.File(label="Download Excel", interactive=False)
+
+            submit_button.click(
+                fn=process_zip,
+                inputs=[zip_input, employee_code_input, debug_input],
+                outputs=[status_output, excel_preview, excel_download],
+            )
+
+        with gr.Tab("Mapping"):
+            product_input = gr.Textbox(
+                label="Product List",
+                placeholder="One product per line",
+                lines=10,
+            )
+
+            with gr.Row():
+                dense_weight_input = gr.Slider(
+                    minimum=0,
+                    maximum=1,
+                    value=0.7,
+                    step=0.1,
+                    label="Dense Weight",
+                )
+                sparse_weight_input = gr.Slider(
+                    minimum=0,
+                    maximum=1,
+                    value=0.3,
+                    step=0.1,
+                    label="Sparse Weight",
+                )
+            gr.Markdown("---")
+            gr.Markdown("#### 💡 Ví dụ")
+            gr.Examples(
+                examples=[
+                    ["Đèn LED ốp trần 18W\nBóng LED E27 9W\nĐèn downlight âm trần"],
+                    ["Bóng Trụ 30W\nPhích nước 2L\nVợt bắt muỗi"],
+                    ["Đèn năng lượng mặt trời\nĐèn sạc tích điện\nBóng compact 20W"],
+                ],
+                inputs=[product_input],
+                label="Click để thử các ví dụ"
+            )
+            normalize_input = gr.Checkbox(label="Normalize Query", value=True)
+            mapping_button = gr.Button("Run Mapping", variant="primary")
+
+            mapping_status = gr.Textbox(label="Status", interactive=False)
+            mapping_preview = gr.Dataframe(label="Mapping Preview", interactive=False)
+
+            mapping_button.click(
+                fn=process_mapping,
+                inputs=[
+                    product_input,
+                    dense_weight_input,
+                    sparse_weight_input,
+                    normalize_input,
+                ],
+                outputs=[mapping_status, mapping_preview],
+            )
+
+
+if __name__ == "__main__":
+    demo.launch(css=custom_css, theme=gr.themes.Base())