Removed extra file

app.py

@@ -1,213 +0,0 @@
-import time
-from io import BytesIO
-
-import pandas as pd
-import streamlit as st
-from gliner2 import GLiNER2
-
-# ---------------------------------------------------------------------------
-# Constants
-# ---------------------------------------------------------------------------
-
-PERSONAL_FIELDS = [
-    "Person Name", "Email Address", "Phone Number",
-    "Street Address", "City", "Country", "Date of Birth",
-]
-PROFESSIONAL_FIELDS = [
-    "Company Name", "Department", "Job Title",
-    "Office Location", "Employee ID", "Skills", "University",
-]
-BUSINESS_FIELDS = [
-    "Counterparty", "Contract Value", "Effective Date", "Jurisdiction",
-    "Governing Law", "Invoice Number", "Product Name", "Project Name",
-]
-ALL_PREDEFINED_FIELDS = PERSONAL_FIELDS + PROFESSIONAL_FIELDS + BUSINESS_FIELDS
-
-MODEL_ID = "fastino/gliner2-base-v1"
-EXTRACTION_THRESHOLD = 0.4
-
-# ---------------------------------------------------------------------------
-# Page config & styles
-# ---------------------------------------------------------------------------
-
-st.set_page_config(
-    page_title="AI Excel Entity Extractor",
-    page_icon="🔍",
-    layout="centered",
-)
-
-st.html("""
-    <style>
-    .stApp { background-color: #fcfcfc; }
-    div.stButton > button:first-child {
-        width: 100%;
-        border-radius: 8px;
-        height: 3.5em;
-        background-color: #2563eb;
-        color: white;
-        font-weight: bold;
-        border: none;
-    }
-    div.stButton > button:hover { background-color: #1d4ed8; border: none; }
-    .footer { text-align: center; color: #64748b; font-size: 0.85rem; margin-top: 50px; }
-    </style>
-""")
-
-# ---------------------------------------------------------------------------
-# Cached resources & helpers
-# ---------------------------------------------------------------------------
-
-@st.cache_resource(show_spinner="Loading AI model…")
-def load_model() -> GLiNER2:
-    return GLiNER2.from_pretrained(MODEL_ID)
-
-
-@st.cache_data(show_spinner=False)
-def load_excel(file) -> pd.DataFrame:
-    return pd.read_excel(file)
-
-
-def to_excel_bytes(df: pd.DataFrame) -> bytes:
-    buf = BytesIO()
-    with pd.ExcelWriter(buf, engine="openpyxl") as writer:
-        df.to_excel(writer, index=False)
-    return buf.getvalue()
-
-
-def parse_custom_labels(raw: str) -> list[str]:
-    return [c.strip() for c in raw.split(",") if c.strip()]
-
-
-def is_valid_text(value: str) -> bool:
-    return bool(value.strip()) and value.lower() != "nan"
-
-# ---------------------------------------------------------------------------
-# UI - Header
-# ---------------------------------------------------------------------------
-
-st.title("🔍 AI Excel Entity Extractor")
-st.markdown(
-    "Automatically extract specific entities like Name, Email, etc., "
-    "from your spreadsheet text using GLiNER2 Zero-Shot AI."
-)
-
-# ---------------------------------------------------------------------------
-# Step 1: Upload
-# ---------------------------------------------------------------------------
-
-st.write("### 1. Source Data")
-uploaded_file = st.file_uploader("Upload an Excel file (.xlsx)", type="xlsx")
-
-if not uploaded_file:
-    st.write("### How it works")
-    col_a, col_b, col_c = st.columns(3)
-    with col_a:
-        st.markdown("**1. Upload**\nDrop an Excel file with a column of text (e.g., emails, descriptions, or notes).")
-    with col_b:
-        st.markdown("**2. Define**\nSelect from common entities like Names and Dates, or type your own custom fields.")
-    with col_c:
-        st.markdown("**3. Extract**\nThe AI reads every row and creates new columns for every entity it discovers.")
-    st.stop()
-
-# ---------------------------------------------------------------------------
-# Step 2: Configure
-# ---------------------------------------------------------------------------
-
-df = load_excel(uploaded_file)
-
-if df.empty:
-    st.error("The uploaded file appears to be empty. Please upload a file with data.")
-    st.stop()
-
-row_count = len(df)
-
-st.divider()
-st.write("### 2. Configure Extraction")
-
-with st.spinner("Loading configuration…"):
-    with st.container(border=True):
-        col_select, col_info = st.columns([2, 1])
-        with col_select:
-            text_column = st.selectbox("Select text column to analyze:", df.columns)
-        with col_info:
-            st.metric("Total Rows", f"{row_count:,}")
-
-        st.write("---")
-
-        col1, col2 = st.columns(2)
-        with col1:
-            selected_labels = st.multiselect(
-                "Select Fields to Extract:",
-                options=ALL_PREDEFINED_FIELDS,
-                default=["Person Name", "Company Name"],
-                help="Choose common entities from the library.",
-            )
-        with col2:
-            custom_labels_str = st.text_area(
-                "Custom Entities (Comma Separated):",
-                placeholder="e.g. Case Number, Part ID, Deadline",
-                help="Define unique entities specific to your data.",
-            )
-
-        active_labels = list(dict.fromkeys(selected_labels + parse_custom_labels(custom_labels_str)))
-
-# ---------------------------------------------------------------------------
-# Step 3: Extract
-# ---------------------------------------------------------------------------
-
-if not st.button("🚀 Extract Fields"):
-    st.stop()
-
-if not active_labels:
-    st.warning("⚠️ Please select or define at least one entity to extract.")
-    st.stop()
-
-model = load_model()
-processed_df = df.copy()
-for label in active_labels:
-    processed_df[label] = ""
-
-status = st.empty()
-progress_bar = st.progress(0)
-start_time = time.time()
-
-for i, row in processed_df.iterrows():
-    text = str(row[text_column])
-    if is_valid_text(text):
-        try:
-            results = model.extract_entities(text, active_labels, threshold=EXTRACTION_THRESHOLD)
-            for label, found_list in results.get("entities", {}).items():
-                processed_df.at[i, label] = ", ".join(found_list)
-        except Exception as e:
-            st.warning(f"Row {i + 1} skipped due to an error: {e}")
-
-    progress_bar.progress((i + 1) / row_count)
-    status.text(f"Extracting fields from row {i + 1} of {row_count}…")
-
-duration = round(time.time() - start_time, 1)
-progress_bar.empty()
-status.empty()
-
-st.success(f"✅ Extraction complete - {row_count:,} rows processed in {duration}s.")
-
-st.write("### 3. Extraction Preview")
-st.dataframe(processed_df.head(10), use_container_width=True)
-
-st.download_button(
-    label="📥 Download Enriched Excel File",
-    data=to_excel_bytes(processed_df),
-    file_name="AI_Extracted_Report.xlsx",
-    mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-)
-
-# ---------------------------------------------------------------------------
-# Footer
-# ---------------------------------------------------------------------------
-
-st.markdown("---")
-st.markdown(
-    '<div class="footer">Powered by '
-    '<a href="https://github.com/fastino-ai/GLiNER2" target="_blank">GLiNER2</a>'
-    " • Open-source Zero-Shot Named Entity Recognition</div>",
-    unsafe_allow_html=True,
-)