04/22 Add support old document *.doc

views/ai_summary_view.py

@@ -37,16 +37,16 @@ logger = logging.getLogger("ai_summary_view")
 SUPPORTED_EXT = {
     ".txt", ".md", ".csv",
     ".pdf",
-    ".docx",
+    ".doc", ".docx",
     ".xlsx", ".xls",
 }
 
 FILE_DIALOG_TYPES = [
     ("Supported documents",
-     "*.txt *.md *.csv *.pdf *.docx *.xlsx *.xls"),
+     "*.txt *.md *.csv *.pdf *.docx *.doc *.xlsx *.xls"),
     ("Text files",        "*.txt *.md *.csv"),
     ("PDF files",         "*.pdf"),
-    ("Word documents",    "*.docx"),
+    ("Word documents",    "*.docx *.doc"),
     ("Excel files",       "*.xlsx *.xls"),
     ("All files",         "*.*"),
 ]
@@ -65,11 +65,18 @@ _CFG_KEY_KEY   = "api_key"
 _CFG_KEY_MODEL = "model"
 
 # -- Focused extraction prompt (procurement / solicitation) --------------------
+_OFFICE_ADDRESS = "2815 Hartland Road, Falls Church, VA 22043, USA"
+
 _EXTRACTION_PROMPT = """\
 You are an expert government procurement analyst.
 The user has provided {n} document(s). Your job is to extract specific \
 information from each document and present it in a clean, structured format.
 
+IMPORTANT — Our office is located at:
+  {office}
+Use this as the ORIGIN address for all driving distance and travel time \
+calculations in field #9 below.
+
 For EACH document, extract and clearly label the following fields \
 (write "N/A" if a field is not found):
 
@@ -78,10 +85,16 @@ For EACH document, extract and clearly label the following fields \
   3. Set-Aside          (e.g. Small Business, 8(a), N/A)
   4. Description / Scope of Work
   5. Work Site / Location(s)
-  6. Pre-Proposal Conference / Site-Visit  (date, time, location — mandatory or optional)
+  6. Pre-Proposal Conference / Site-Visit  (date, time, full address — mandatory or optional)
   7. Point of Contact (POC)  (name, phone, email)
   8. Total Square Footage    (if applicable)
-  9. Driving Distance / Travel Time to Pre-Proposal Conference
+  9. Driving Distance & Travel Time
+     - Origin: {office}
+     - Destination: the Pre-Proposal Conference or Site-Visit address from field #6 \
+(if no conference, use the primary Work Site address from field #5)
+     - Provide your best estimate of driving distance (miles) and typical \
+driving time using major highways
+     - Note that these are AI estimates; actual times may vary with traffic
  10. Last Day to Submit Questions
  11. Due Date & Time
  12. Any other notable requirements or deadlines
@@ -626,7 +639,7 @@ class AiSummaryView(ttk.Frame):
             # 2. Build prompt
             n = len(file_paths)
             prompt = _EXTRACTION_PROMPT.format(
-                n=n, documents=combined_text)
+                n=n, office=_OFFICE_ADDRESS, documents=combined_text)
 
             # 3. Call Groq API
             self.after(0, lambda: self._set_status(
@@ -715,6 +728,8 @@ def _extract_text(file_path: str) -> str:
         return _read_pdf(file_path)
     if ext == ".docx":
         return _read_docx(file_path)
+    if ext == ".doc":
+        return _read_doc(file_path)
     if ext in (".xlsx", ".xls"):
         return _read_excel(file_path)
     raise ValueError(f"Unsupported file type: {ext}")
@@ -750,6 +765,7 @@ def _read_pdf(path: str) -> str:
 
 
 def _read_docx(path: str) -> str:
+    """Read modern .docx (Office Open XML) via python-docx."""
     try:
         import docx
         doc = docx.Document(path)
@@ -761,6 +777,76 @@ def _read_docx(path: str) -> str:
         )
 
 
+def _read_doc(path: str) -> str:
+    """Read legacy .doc (binary OLE) files.
+
+    Strategy (Windows):
+      1. win32com — automates Word to save as temp .docx, then reads it.
+         Requires Microsoft Word to be installed; most reliable.
+      2. docx2txt — pure-Python fallback that can sometimes handle simple .doc
+         files by treating them as ZIP-like structures (works for some .doc).
+      3. Raw text scrape — last resort ASCII extraction from the binary.
+    """
+    # --- Strategy 1: Word COM automation (Windows + Word installed) -----------
+    try:
+        import win32com.client
+        import tempfile
+        import pythoncom
+        pythoncom.CoInitialize()
+        word = win32com.client.Dispatch("Word.Application")
+        word.Visible = False
+        try:
+            abs_path = os.path.abspath(path)
+            doc = word.Documents.Open(abs_path)
+            # Save as a temp .docx so python-docx can read it cleanly
+            with tempfile.NamedTemporaryFile(suffix=".docx", delete=False) as tf:
+                tmp_path = tf.name
+            doc.SaveAs2(tmp_path, FileFormat=16)  # 16 = wdFormatXMLDocument
+            doc.Close(False)
+            text = _read_docx(tmp_path)
+            return text
+        finally:
+            try:
+                word.Quit()
+            except Exception:
+                pass
+            try:
+                os.unlink(tmp_path)
+            except Exception:
+                pass
+            pythoncom.CoUninitialize()
+    except Exception:
+        pass  # Word not installed or COM error — try next strategy
+
+    # --- Strategy 2: docx2txt (pure Python, works on some .doc files) --------
+    try:
+        import docx2txt
+        text = docx2txt.process(path)
+        if text and text.strip():
+            return text
+    except Exception:
+        pass
+
+    # --- Strategy 3: raw ASCII scrape from binary ----------------------------
+    try:
+        with open(path, "rb") as f:
+            raw = f.read()
+        # Extract printable ASCII runs of 4+ chars
+        import re
+        chunks = re.findall(rb"[ -~]{4,}", raw)
+        text = "\n".join(c.decode("ascii", errors="ignore") for c in chunks)
+        if text.strip():
+            return text
+    except Exception:
+        pass
+
+    raise ValueError(
+        f"Could not extract text from '{os.path.basename(path)}'.\n"
+        "For best results with .doc files, install Microsoft Word "
+        "or convert the file to .docx before uploading."
+    )
+
+
 def _read_excel(path: str) -> str:
     try:
         import openpyxl
@@ -787,6 +873,7 @@ def _file_icon(path: str) -> str:
     ext = os.path.splitext(path)[1].lower()
     return {
         ".pdf":  "📄",
+        ".doc":  "📝",
         ".docx": "📝",
         ".xlsx": "📊",
         ".xls":  "📊",