Upload app.py

app.py

@@ -1,10 +1,8 @@
-
 import gradio as gr
 import json
 import os
 import time
 import tempfile
-import subprocess
 import re
 import requests
 from pathlib import Path
@@ -13,18 +11,8 @@ from typing import List, Dict
 from difflib import SequenceMatcher
 from huggingface_hub import InferenceClient
 from gtts import gTTS
-
-subprocess.run(["pip", "install", "pypdf", "-q"], capture_output=True)
-
-try:
-    from pypdf import PdfReader
-except Exception:
-    PdfReader = None
-
-try:
-    import PyPDF2
-except Exception:
-    PyPDF2 = None
+from pypdf import PdfReader
+import PyPDF2
 
 
 SAVE_DIR = Path("/data/saved_docs")
@@ -33,8 +21,12 @@ SAVE_DIR.mkdir(parents=True, exist_ok=True)
 MODEL = "mistralai/Mistral-7B-Instruct-v0.3"
 
 
+# =========================
+# SHARED: TOKEN / MODEL / TTS
+# =========================
+
 def get_client():
-    token = os.environ.get("HF_TOKEN", "") or os.environ.get("HUGGINGFACE_TOKEN", "")
+    token = os.environ.get("HF_TOKEN", "")
     return InferenceClient(model=MODEL, token=token if token else None)
 
 
@@ -46,12 +38,9 @@ def call_llama(prompt, max_tokens=1200):
             max_tokens=max_tokens,
             temperature=0.4,
         )
-
         if hasattr(response, "choices") and response.choices:
             return response.choices[0].message.content
-
         return f"No model output returned. Raw response: {response}"
-
     except Exception as e:
         return f"Model error: {e}"
 
@@ -60,47 +49,37 @@ def make_tts(text):
     try:
         if not text or not str(text).strip():
             return None
-
         clean_text = str(text).strip()[:5000]
-
         tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
         tmp.close()
-
         tts = gTTS(text=clean_text, lang="en", slow=False)
         tts.save(tmp.name)
-
         return tmp.name
-
     except Exception as e:
         print(f"TTS error: {e}")
         return None
 
 
+# =========================
+# METABOLIC PHOENIX
+# =========================
+
 def extract_pdf_text(path):
     if not path:
         return ""
-
     if hasattr(path, "name"):
         path = path.name
-
     try:
-        if PdfReader is None:
-            return "[PDF reader unavailable]"
-
         reader = PdfReader(path)
         pages = []
-
         for i, page in enumerate(reader.pages):
             text = page.extract_text() or ""
             if text.strip():
                 pages.append(f"--- Page {i + 1} ---\n{text.strip()}")
-
         if not pages:
             return "[No extractable text — may be a scanned PDF]"
-
         full = "\n\n".join(pages)
         return full[:14000] + "\n\n[...truncated]" if len(full) > 14000 else full
-
     except Exception as e:
         return f"[PDF read error: {e}]"
 
@@ -179,28 +158,18 @@ def save_phoenix_analysis(r1, r2, r3, r4):
 
     try:
         index_path = SAVE_DIR / "index.json"
-        library = json.loads(index_path.read_text()) if index_path.exists() else []
-
-        entry = {
-            "id": doc_id,
-            "title": f"Analysis — {ts}",
-            "content": content,
-            "saved_at": ts,
-        }
-
+        library = json.loads(index_path.read_text(encoding="utf-8")) if index_path.exists() else []
+        entry = {"id": doc_id, "title": f"Analysis — {ts}", "content": content, "saved_at": ts}
         library.insert(0, entry)
         index_path.write_text(json.dumps(library, indent=2), encoding="utf-8")
         (SAVE_DIR / f"{doc_id}.txt").write_text(content, encoding="utf-8")
-
         return f"Saved: Analysis — {ts}"
-
     except Exception as e:
         return f"Save error: {e}"
 
 
 def load_library():
     index_path = SAVE_DIR / "index.json"
-
     try:
         return json.loads(index_path.read_text(encoding="utf-8")) if index_path.exists() else []
     except Exception:
@@ -213,7 +182,6 @@ def lib_choices(library):
 
 def choice_to_entry(library, choice):
     choices = lib_choices(library)
-
     try:
         return library[choices.index(choice)]
     except Exception:
@@ -227,73 +195,56 @@ def lib_refresh():
 
 def lib_view(selection):
     library = load_library()
-
     if not selection:
         return ""
-
     entry = choice_to_entry(library, selection)
     return entry["content"] if entry else "Not found."
 
 
 def lib_download(selection):
     library = load_library()
-
     if not selection:
         return gr.update(visible=False), "Select a document first."
-
     entry = choice_to_entry(library, selection)
-
     if not entry:
         return gr.update(visible=False), "Not found."
-
     file_path = SAVE_DIR / f"{entry['id']}.txt"
-
     if file_path.exists():
         return gr.update(value=str(file_path), visible=True), "Ready."
-
     return gr.update(visible=False), "File missing."
 
 
 def lib_delete(selection):
     library = load_library()
-
     if not selection:
         return gr.update(choices=lib_choices(library), value=None), "Select a document."
-
     entry = choice_to_entry(library, selection)
-
     if not entry:
         return gr.update(choices=lib_choices(library), value=None), "Not found."
-
     library = [x for x in library if x["id"] != entry["id"]]
     (SAVE_DIR / "index.json").write_text(json.dumps(library, indent=2), encoding="utf-8")
-
     file_path = SAVE_DIR / f"{entry['id']}.txt"
-
     if file_path.exists():
         file_path.unlink()
-
     return gr.update(choices=lib_choices(library), value=None), "Deleted."
 
 
+# =========================
+# SEAMSTRESSES DOCUMENT PROCESSING
+# =========================
+
 class DocumentParser:
     @staticmethod
     def extract_text_from_pdf(file_path: str) -> str:
         try:
-            if PyPDF2 is None:
-                return "Error reading PDF: PyPDF2 unavailable"
-
             with open(file_path, "rb") as f:
                 reader = PyPDF2.PdfReader(f)
                 text = ""
-
                 for page in reader.pages:
                     extracted = page.extract_text()
                     if extracted:
                         text += extracted + "\n"
-
             return text
-
         except Exception as e:
             return f"Error reading PDF: {str(e)}"
 
@@ -301,10 +252,8 @@ class DocumentParser:
     def extract_text_from_docx(file_path: str) -> str:
         try:
             from docx import Document
-
             doc = Document(file_path)
             return "\n\n".join([p.text for p in doc.paragraphs if p.text.strip()])
-
         except Exception as e:
             return f"Error reading DOCX: {str(e)}"
 
@@ -312,19 +261,14 @@ class DocumentParser:
     def extract_text_from_file(file_path: str) -> str:
         if not file_path:
             return "Error: No file path provided"
-
         try:
             lower = file_path.lower()
-
             if lower.endswith(".pdf"):
                 return DocumentParser.extract_text_from_pdf(file_path)
-
             if lower.endswith(".docx"):
                 return DocumentParser.extract_text_from_docx(file_path)
-
             with open(file_path, "r", encoding="utf-8", errors="ignore") as f:
                 return f.read()
-
         except Exception as e:
             return f"Error reading file: {str(e)}"
 
@@ -332,10 +276,8 @@ class DocumentParser:
     def parse_document(text: str) -> Dict:
         paragraphs = [p.strip() for p in text.split("\n\n") if p.strip()]
         sentences = []
-
         for paragraph in paragraphs:
             sentences.extend([s.strip() + "." for s in paragraph.split(".") if s.strip()])
-
         return {
             "full_text": text,
             "paragraphs": paragraphs,
@@ -356,82 +298,62 @@ class ForensicAnalyzer:
     @staticmethod
     def find_duplicates(docs: List[Dict]) -> List[Dict]:
         duplicates = []
-
         for i, doc1 in enumerate(docs):
             for j, doc2 in enumerate(docs[i + 1:], start=i + 1):
                 for para1 in doc1["paragraphs"]:
                     for para2 in doc2["paragraphs"]:
                         if len(para1) < 20 or len(para2) < 20:
                             continue
-
                         ratio = SequenceMatcher(None, para1, para2).ratio()
-
                         if ratio > 0.85:
-                            duplicates.append(
-                                {
-                                    "doc1": i,
-                                    "doc2": j,
-                                    "similarity": ratio,
-                                    "text": para1[:5000] + "...",
-                                    "type": "duplicate" if ratio > 0.95 else "near_duplicate",
-                                }
-                            )
-
+                            duplicates.append({
+                                "doc1": i,
+                                "doc2": j,
+                                "similarity": ratio,
+                                "text": para1[:5000] + "...",
+                                "type": "duplicate" if ratio > 0.95 else "near_duplicate",
+                            })
         return duplicates
 
     @staticmethod
     def find_unique_content(docs: List[Dict]) -> List[Dict]:
         unique_items = []
-
         for i, doc in enumerate(docs):
             for para in doc["paragraphs"]:
                 if len(para) < 20:
                     continue
-
                 is_unique = True
-
                 for j, other_doc in enumerate(docs):
                     if i == j:
                         continue
-
                     for other_para in other_doc["paragraphs"]:
                         if SequenceMatcher(None, para, other_para).ratio() > 0.8:
                             is_unique = False
                             break
-
                     if not is_unique:
                         break
-
                 if is_unique:
-                    unique_items.append(
-                        {
-                            "doc_index": i,
-                            "content": para,
-                            "length": len(para),
-                            "type": "unique_paragraph",
-                        }
-                    )
-
+                    unique_items.append({
+                        "doc_index": i,
+                        "content": para,
+                        "length": len(para),
+                        "type": "unique_paragraph",
+                    })
         return unique_items
 
     @staticmethod
     def detect_risk_language(text: str) -> List[Dict]:
         findings = []
-
         for risk_type, pattern in ForensicAnalyzer.RISK_PATTERNS.items():
             for match in re.finditer(pattern, text, re.IGNORECASE):
                 start = max(0, match.start() - 50)
                 end = min(len(text), match.end() + 50)
-
-                findings.append(
-                    {
-                        "type": risk_type,
-                        "matched_text": match.group(),
-                        "context": text[start:end],
-                        "position": match.start(),
-                    }
-                )
-
+                findings.append({
+                    "type": risk_type,
+                    "matched_text": match.group(),
+                    "context": text[start:end],
+                    "position": match.start(),
+                })
         return findings
 
     @staticmethod
@@ -452,84 +374,63 @@ class DocumentMerger:
     def select_best_content(duplicates, docs):
         best_versions = []
         processed = set()
-
         for duplicate in duplicates:
             key = tuple(sorted([duplicate["doc1"], duplicate["doc2"]]))
-
             if key in processed:
                 continue
-
             processed.add(key)
-
             doc1_text = docs[duplicate["doc1"]]["full_text"]
             doc2_text = docs[duplicate["doc2"]]["full_text"]
-
             best_versions.append(doc1_text if len(doc1_text) > len(doc2_text) else doc2_text)
-
         return best_versions
 
     @staticmethod
     def merge_documents(docs, analysis):
         output = []
-
         output.append("MASTER EVIDENCE DOCUMENT")
         output.append("=" * 80)
         output.append(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
         output.append(f"Source Documents: {len(docs)}")
         output.append("=" * 80 + "\n")
-
         output.append("EXECUTIVE SUMMARY")
         output.append("-" * 80)
         output.append(f"Total Duplicates Found: {len(analysis['duplicates'])}")
         output.append(f"Unique Content Items: {len(analysis['unique_content'])}")
         output.append(f"Risk Language Instances: {len(analysis['risk_language'])}\n")
-
         output.append("CONSOLIDATED CONTENT")
         output.append("-" * 80)
-
         best_versions = DocumentMerger.select_best_content(analysis["duplicates"], docs)
-
         if best_versions:
             for i, content in enumerate(best_versions, 1):
                 output.append(f"\n[Section {i}]")
                 output.append(content[:50000] + "..." if len(content) > 50000 else content)
         else:
             output.append("No duplicate content found across documents.")
-
         output.append("\n\nUNIQUE FINDINGS")
         output.append("-" * 80)
-
         for item in analysis["unique_content"][:100]:
             output.append(f"\n[From Document {item['doc_index'] + 1}]")
             content = item["content"]
             output.append(content[:50000] + "..." if len(content) > 50000 else content)
-
         output.append("\n\nRISK LANGUAGE ANALYSIS")
         output.append("-" * 80)
-
         risk_summary = {}
-
         for risk in analysis["risk_language"]:
             risk_summary[risk["type"]] = risk_summary.get(risk["type"], 0) + 1
-
         if risk_summary:
             for risk_type, count in risk_summary.items():
                 output.append(f"{risk_type.replace('_', ' ').title()}: {count} instances")
         else:
             output.append("No risk language patterns detected.")
-
         output.append("\n" + "=" * 80)
         output.append("END OF MASTER EVIDENCE DOCUMENT")
-
         return "\n".join(output)
 
 
 def ai_summarise(merged_doc: str, analysis: Dict) -> str:
     try:
-        hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
-
+        hf_token = os.environ.get("HF_TOKEN")
         risk_summary = {}
-
         for risk in analysis.get("risk_language", []):
             risk_summary[risk["type"]] = risk_summary.get(risk["type"], 0) + 1
 
@@ -544,7 +445,6 @@ Document excerpt:
 """.strip()
 
         headers = {"Content-Type": "application/json"}
-
         if hf_token:
             headers["Authorization"] = f"Bearer {hf_token}"
 
@@ -564,18 +464,12 @@ Document excerpt:
 
         if response.status_code == 200:
             data = response.json()
-
             if isinstance(data, list) and data:
                 summary = data[0].get("summary_text", "")
                 risk_lines = "\n".join(
-                    [
-                        f"- {k.replace('_', ' ').title()}: {v} instances"
-                        for k, v in risk_summary.items()
-                    ]
+                    [f"- {k.replace('_', ' ').title()}: {v} instances" for k, v in risk_summary.items()]
                 )
-
                 return f"AI SUMMARY\n{'=' * 60}\n\n{summary}\n\nRisk Language Breakdown:\n{risk_lines}"
-
             return "Summary could not be extracted from model response."
 
         if response.status_code == 503:
@@ -590,7 +484,6 @@ Document excerpt:
 def export_txt(merged_doc: str, analysis_report: str) -> str:
     try:
         tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".txt", mode="w", encoding="utf-8")
-
         tmp.write("SEAMSTRESSES FRAMEWORK — EXPORT\n")
         tmp.write(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
         tmp.write("=" * 80 + "\n\n")
@@ -598,9 +491,7 @@ def export_txt(merged_doc: str, analysis_report: str) -> str:
         tmp.write("\n\n" + "=" * 80 + "\n\n")
         tmp.write(analysis_report)
         tmp.close()
-
         return tmp.name
-
     except Exception:
         return None
 
@@ -627,15 +518,7 @@ def export_pdf(merged_doc: str, analysis_report: str) -> str:
         )
 
         styles = getSampleStyleSheet()
-
-        title_style = ParagraphStyle(
-            "Title",
-            parent=styles["Heading1"],
-            fontSize=16,
-            alignment=TA_CENTER,
-            spaceAfter=6,
-        )
-
+        title_style = ParagraphStyle("Title", parent=styles["Heading1"], fontSize=16, alignment=TA_CENTER, spaceAfter=6)
         heading_style = ParagraphStyle(
             "Heading",
             parent=styles["Heading2"],
@@ -643,17 +526,9 @@ def export_pdf(merged_doc: str, analysis_report: str) -> str:
             textColor=colors.HexColor("#1a1a2e"),
             spaceAfter=4,
         )
-
-        body_style = ParagraphStyle(
-            "Body",
-            parent=styles["Normal"],
-            fontSize=9,
-            leading=14,
-            spaceAfter=3,
-        )
+        body_style = ParagraphStyle("Body", parent=styles["Normal"], fontSize=9, leading=14, spaceAfter=3)
 
         story = []
-
         story.append(Paragraph("Seamstresses Framework", title_style))
         story.append(Paragraph("Forensic Document Analysis Report", heading_style))
         story.append(Paragraph(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", body_style))
@@ -664,7 +539,6 @@ def export_pdf(merged_doc: str, analysis_report: str) -> str:
         for line in merged_doc.split("\n"):
             if line.strip():
                 safe = line.replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;")
-
                 if line.startswith("===") or line.startswith("---"):
                     story.append(HRFlowable(width="100%", thickness=0.5, color=colors.lightgrey))
                 elif line.isupper() and len(line) > 3 and not line.startswith("["):
@@ -679,17 +553,15 @@ def export_pdf(merged_doc: str, analysis_report: str) -> str:
         for line in analysis_report.split("\n"):
             if line.strip():
                 safe = line.replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;")
-
                 if line.isupper() and len(line) > 3:
                     story.append(Paragraph(safe, heading_style))
                 else:
                     story.append(Paragraph(safe, body_style))
 
         doc.build(story)
-
         return tmp.name
-
-    except Exception:
+    except Exception as e:
+        print(f"PDF export error: {e}")
         return None
 
 
@@ -702,7 +574,6 @@ def export_docx(merged_doc: str, analysis_report: str, summary: str) -> str:
         tmp.close()
 
         doc = DocxDocument()
-
         title = doc.add_heading("Seamstresses Framework", 0)
         title.alignment = WD_ALIGN_PARAGRAPH.CENTER
 
@@ -739,10 +610,9 @@ def export_docx(merged_doc: str, analysis_report: str, summary: str) -> str:
                     doc.add_paragraph(line)
 
         doc.save(tmp.name)
-
         return tmp.name
-
-    except Exception:
+    except Exception as e:
+        print(f"DOCX export error: {e}")
         return None
 
 
@@ -754,7 +624,6 @@ def process_documents(file1, file2, file3):
         for uploaded in raw_inputs:
             if uploaded is None:
                 continue
-
             if isinstance(uploaded, dict):
                 path = uploaded.get("path") or uploaded.get("name")
             elif hasattr(uploaded, "name"):
@@ -763,7 +632,6 @@ def process_documents(file1, file2, file3):
                 path = uploaded
             else:
                 path = str(uploaded)
-
             if path:
                 files.append(path)
 
@@ -775,10 +643,8 @@ def process_documents(file1, file2, file3):
 
         for file_path in files:
             text = DocumentParser.extract_text_from_file(file_path)
-
             if not text or text.startswith("Error"):
                 return f"Error: {text}", "", "", None, None, None
-
             docs.append(DocumentParser.parse_document(text))
             file_names.append(os.path.basename(file_path))
 
@@ -787,7 +653,6 @@ def process_documents(file1, file2, file3):
 
         analysis_report = f"FORENSIC ANALYSIS REPORT\n{'=' * 80}\n\n"
         analysis_report += f"FILES ANALYZED: {', '.join(file_names)}\n\n"
-
         analysis_report += f"DUPLICATE ANALYSIS\n{'-' * 80}\n"
         analysis_report += f"Total Duplicates Found: {len(analysis['duplicates'])}\n\n"
 
@@ -804,7 +669,6 @@ def process_documents(file1, file2, file3):
             analysis_report += f"- From Document {item['doc_index'] + 1}: {item['content'][:5000]}...\n"
 
         analysis_report += f"\nRISK LANGUAGE DETECTED\n{'-' * 80}\n"
-
         risk_types = {}
 
         for risk in analysis["risk_language"]:
@@ -826,13 +690,14 @@ def process_documents(file1, file2, file3):
 
     except Exception as e:
         import traceback
-
         return f"Error: {str(e)}\n\n{traceback.format_exc()}", "", "", None, None, None
 
 
-CSS = """
-@import url('https://fonts.googleapis.com/css2?family=Syne:wght@400;700;800&family=DM+Mono:wght@400;500&display=swap');
+# =========================
+# UI
+# =========================
 
+CSS = """
 :root {
     --bg: #030712;
     --surface: #0a0f1e;
@@ -848,7 +713,6 @@ CSS = """
 
 body, .gradio-container {
     background: var(--bg) !important;
-    font-family: 'Syne', sans-serif !important;
     color: var(--text) !important;
 }
 
@@ -856,31 +720,23 @@ body, .gradio-container {
     text-align: center;
     padding: 2.4rem 1rem 1.6rem;
     border-bottom: 1px solid var(--border);
-    background:
-        radial-gradient(ellipse at 30% 50%, rgba(0,102,255,0.12) 0%, transparent 60%),
-        radial-gradient(ellipse at 70% 50%, rgba(0,207,255,0.08) 0%, transparent 60%);
 }
 
 .hdr h1 {
     font-size: 2.2rem;
     font-weight: 800;
-    letter-spacing: -0.04em;
-    background: linear-gradient(135deg, var(--accent) 0%, var(--accent2) 100%);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
+    color: var(--accent);
     margin: 0 0 0.3rem;
 }
 
 .hdr .sub {
     color: var(--muted);
-    font-family: 'DM Mono', monospace;
     font-size: .75rem;
     letter-spacing: .06em;
 }
 
 .hdr .own {
     color: var(--accent2);
-    font-family: 'DM Mono', monospace;
     font-size: .68rem;
     margin-top: .3rem;
     letter-spacing: .1em;
@@ -892,7 +748,6 @@ textarea, input[type=text] {
     border: 1px solid var(--border) !important;
     border-radius: var(--r) !important;
     color: var(--text) !important;
-    font-family: 'DM Mono', monospace !important;
 }
 
 button.primary {
@@ -900,7 +755,6 @@ button.primary {
     color: #030712 !important;
     border: none !important;
     border-radius: var(--r) !important;
-    font-family: 'Syne', sans-serif !important;
     font-weight: 800 !important;
 }
 
@@ -909,14 +763,12 @@ button.secondary {
     color: var(--accent2) !important;
     border: 1px solid var(--accent3) !important;
     border-radius: var(--r) !important;
-    font-family: 'Syne', sans-serif !important;
     font-weight: 700 !important;
 }
 
 .out textarea {
     background: #020510 !important;
     border-left: 3px solid var(--accent) !important;
-    font-family: 'DM Mono', monospace !important;
     font-size: .84rem !important;
     line-height: 1.7 !important;
 }
@@ -925,7 +777,6 @@ button.secondary {
     text-align: center;
     padding: 1.5rem;
     color: var(--muted);
-    font-family: 'DM Mono', monospace;
     font-size: .68rem;
     border-top: 1px solid var(--border);
     margin-top: 2rem;
@@ -983,12 +834,7 @@ with gr.Blocks(css=CSS, title="The Metamorphic Phoenix") as demo:
                     value=None,
                     interactive=True,
                 )
-                lib_viewer = gr.Textbox(
-                    label="Content",
-                    lines=16,
-                    elem_classes=["out"],
-                    interactive=False,
-                )
+                lib_viewer = gr.Textbox(label="Content", lines=16, elem_classes=["out"], interactive=False)
 
                 with gr.Row():
                     lib_dl_btn = gr.Button("Download", variant="secondary")
@@ -997,11 +843,7 @@ with gr.Blocks(css=CSS, title="The Metamorphic Phoenix") as demo:
                 lib_dl_file = gr.File(label="Your file", visible=False)
                 lib_msg = gr.Markdown("")
 
-            run_btn.click(
-                phoenix_analyse,
-                inputs=[pdf_input],
-                outputs=[out1, out2, out3, out4],
-            )
+            run_btn.click(phoenix_analyse, inputs=[pdf_input], outputs=[out1, out2, out3, out4])
 
             top_tts_btn.click(
                 lambda a, b, c, d: make_tts(
@@ -1019,32 +861,20 @@ with gr.Blocks(css=CSS, title="The Metamorphic Phoenix") as demo:
             tts3_btn.click(make_tts, inputs=[out3], outputs=[tts3_audio])
             tts4_btn.click(make_tts, inputs=[out4], outputs=[tts4_audio])
 
-            save_btn.click(
-                save_phoenix_analysis,
-                inputs=[out1, out2, out3, out4],
-                outputs=[save_msg],
-            )
-
+            save_btn.click(save_phoenix_analysis, inputs=[out1, out2, out3, out4], outputs=[save_msg])
             lib_ref_btn.click(lib_refresh, inputs=[], outputs=[lib_dd, lib_msg])
             lib_dd.change(lib_view, inputs=[lib_dd], outputs=lib_viewer)
             lib_dl_btn.click(lib_download, inputs=[lib_dd], outputs=[lib_dl_file, lib_msg])
             lib_del_btn.click(lib_delete, inputs=[lib_dd], outputs=[lib_dd, lib_msg])
 
         with gr.TabItem("Seamstresses Framework"):
-            gr.Markdown(
-                """
-                # Seamstresses Framework
+            gr.Markdown("# Seamstresses Framework\n\nForensic document analysis, intelligent merging, risk-language detection, AI summary, and export.")
 
-                Forensic document analysis, intelligent merging, risk-language detection, AI summary, and export.
-                """
-            )
+            file1 = gr.File(label="Document 1", file_types=[".pdf", ".txt", ".docx"], type="filepath")
+            file2 = gr.File(label="Document 2", file_types=[".pdf", ".txt", ".docx"], type="filepath")
+            file3 = gr.File(label="Document 3 Optional", file_types=[".pdf", ".txt", ".docx"], type="filepath")
 
-            with gr.Row():
-                with gr.Column():
-                    file1 = gr.File(label="Document 1", file_types=[".pdf", ".txt", ".docx"], type="filepath")
-                    file2 = gr.File(label="Document 2", file_types=[".pdf", ".txt", ".docx"], type="filepath")
-                    file3 = gr.File(label="Document 3 Optional", file_types=[".pdf", ".txt", ".docx"], type="filepath")
-                    analyze_btn = gr.Button("Analyze, Merge & Summarise", variant="primary", size="lg")
+            analyze_btn = gr.Button("Analyze, Merge & Summarise", variant="primary", size="lg")
 
             with gr.Tabs():
                 with gr.TabItem("Master Document"):
@@ -1057,16 +887,13 @@ with gr.Blocks(css=CSS, title="The Metamorphic Phoenix") as demo:
                     summary_output = gr.Textbox(label="AI Summary", lines=30, max_lines=200)
 
                 with gr.TabItem("Export"):
-                    with gr.Row():
-                        txt_file = gr.File(label="Plain Text", interactive=False)
-                        pdf_file = gr.File(label="PDF", interactive=False)
-                        docx_file = gr.File(label="Word Document", interactive=False)
-
-                    with gr.Row():
-                        tts_merged_btn = gr.Button("Voice: Master Document", variant="secondary")
-                        tts_analysis_btn = gr.Button("Voice: Analysis Report", variant="secondary")
-                        tts_summary_btn = gr.Button("Voice: AI Summary", variant="secondary")
+                    txt_file = gr.File(label="Plain Text", interactive=False)
+                    pdf_file = gr.File(label="PDF", interactive=False)
+                    docx_file = gr.File(label="Word Document", interactive=False)
 
+                    tts_merged_btn = gr.Button("Voice: Master Document", variant="secondary")
+                    tts_analysis_btn = gr.Button("Voice: Analysis Report", variant="secondary")
+                    tts_summary_btn = gr.Button("Voice: AI Summary", variant="secondary")
                     tts_seam_audio = gr.Audio(label="TTS Playback", type="filepath")
 
             analyze_btn.click(