Added main file

src/app.py

@@ -0,0 +1,213 @@
+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,
+)