Update app.py

app.py

@@ -30,15 +30,15 @@ _MODELS = [
 def call_llama(prompt: str, max_tokens: int = 2000) -> str:
     token = os.environ.get("HF_TOKEN", "")
     headers = {"Authorization": f"Bearer {token}"} if token else {}
+    
     for model in _MODELS:
-        for attempt in range(4):
+        for attempt in range(3):
             try:
-                fmt = prompt
                 resp = _req.post(
                     _FREE_API.format(model=model),
                     headers=headers,
                     json={
-                        "inputs": fmt,
+                        "inputs": prompt,
                         "parameters": {
                             "max_new_tokens": max_tokens,
                             "temperature": 0.7,
@@ -47,8 +47,9 @@ def call_llama(prompt: str, max_tokens: int = 2000) -> str:
                         },
                         "options": {"wait_for_model": True}
                     },
-                    timeout=180
+                    timeout=60
                 )
+                
                 if resp.status_code == 200:
                     result = resp.json()
                     if isinstance(result, list) and result:
@@ -56,16 +57,17 @@ def call_llama(prompt: str, max_tokens: int = 2000) -> str:
                         if text:
                             return text
                 elif resp.status_code == 503:
-                    time.sleep(30 * (attempt + 1))
+                    time.sleep(15 * (attempt + 1))
                     continue
                 elif resp.status_code in [429, 402]:
                     break
                 else:
-                    time.sleep(10)
+                    time.sleep(5)
                     continue
-            except Exception:
-                time.sleep(10)
+            except Exception as e:
+                time.sleep(5)
                 continue
+    
     return "Could not reach AI. Models may be loading — please try again in 30 seconds."
 
 # =============================================================================
@@ -81,7 +83,7 @@ def extract_pdf_text(path):
                 pages.append(f"--- Page {i+1} ---\n{text.strip()}")
         return "\n\n".join(pages) if pages else "[No extractable text — may be scanned PDF]"
     except Exception as e:
-        return f"[PDF read error: {e}]"
+        return f"[PDF read error: {str(e)[:100]}]"
 
 def extract_image_text(path):
     try:
@@ -89,7 +91,7 @@ def extract_image_text(path):
         text = pytesseract.image_to_string(img, lang="eng")
         return f"[IMAGE OCR]\n{text.strip()}" if text.strip() else "[Image: no text detected]"
     except Exception as e:
-        return f"[Image OCR error: {e}]"
+        return f"[Image OCR error: {str(e)[:100]}]"
 
 def transcribe_audio(path):
     try:
@@ -97,13 +99,14 @@ def transcribe_audio(path):
         result = model.transcribe(path, language="en")
         return f"[AUDIO TRANSCRIPTION]\n{result['text'].strip()}"
     except Exception as e:
-        return f"[Transcription error: {e}]"
+        return f"[Transcription error: {str(e)[:100]}]"
 
 def extract_content(file):
     if not file:
         return ""
     path = file.name if hasattr(file, "name") else str(file)
     ext = Path(path).suffix.lower()
+    
     if ext == ".pdf":
         return extract_pdf_text(path)
     elif ext in [".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp", ".tiff"]:
@@ -117,9 +120,12 @@ def extract_content(file):
                            capture_output=True, timeout=120)
             return transcribe_audio(tmp.name)
         except Exception as e:
-            return f"[Video extraction error: {e}]"
+            return f"[Video extraction error: {str(e)[:100]}]"
     elif ext in [".txt", ".md"]:
-        return Path(path).read_text(encoding="utf-8", errors="replace")
+        try:
+            return Path(path).read_text(encoding="utf-8", errors="replace")
+        except Exception as e:
+            return f"[Text read error: {str(e)[:100]}]"
     else:
         return f"[Unsupported file type: {ext}]"
 
@@ -196,9 +202,8 @@ def analyse(files):
     r4 = call_llama(
         f"You are Layoutbot — part of the Metabolic Phoenix Framework "
         f"by Dwayne Anthony Brian Galloway.\n\n"
-        f"FACT LEDGER:\n{r1[:1500]}\n\n"
-        f"BREACH ANALYSIS:\n{r2[:1500]}\n\n"
-        f"LOOP OUTPUT:\n{r3[:1500]}\n\n"
+        f"Fact Ledger:\n{r1[:1500]}\n\nBreach Analysis:\n{r2[:1500]}\n\n"
+        f"Loop Output:\n{r3[:1000]}\n\nCase Summary Context Complete\n\n"
         f"{'='*60}\n\n"
         "Produce a complete litigation-ready case summary. "
         "Breaches have occurred and are on the record. Write accordingly.\n\n"
@@ -207,13 +212,27 @@ def analyse(files):
         "TOP 5 ARGUMENTS — strongest first, each grounded in confirmed fact and statute\n"
         "REQUIRED NEXT STEPS — mandatory actions in priority order\n"
         "ESCALATION MAP — which body receives what, by what deadline\n\n"
-        "Ms. Nala | Injectiv8 | Dwayne Anthony Brian Galloway" + PLAIN_ENGLISH
+        "Ms. Nala | Injectiv8 | Dwayne Anthony Brian Galloway" + PLAIN_ENGLISH,
+        max_tokens=2500
     )
     return r1, r2, r3, r4
 
 # =============================================================================
 # TTS (edge-tts + pydub)
 # =============================================================================
+_event_loop = None
+
+def _get_event_loop():
+    global _event_loop
+    try:
+        loop = asyncio.get_running_loop()
+        return loop
+    except RuntimeError:
+        if _event_loop is None or _event_loop.is_closed():
+            _event_loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(_event_loop)
+        return _event_loop
+
 async def _edge_speak(text, path, voice):
     await edge_tts.Communicate(text, voice).save(path)
 
@@ -265,11 +284,11 @@ def text_to_speech_full(text, speed=1.0, pitch=1.0):
         return None
     chunks = _split_chunks(text)
     voices = ["en-GB-SoniaNeural", "en-GB-RyanNeural"]
+    
     for voice in voices:
         files = []
         try:
-            loop = asyncio.new_event_loop()
-            asyncio.set_event_loop(loop)
+            loop = _get_event_loop()
             for i, chunk in enumerate(chunks):
                 out = tempfile.NamedTemporaryFile(delete=False, suffix=f"_{i}.mp3", dir="/tmp")
                 out.close()
@@ -282,13 +301,14 @@ def text_to_speech_full(text, speed=1.0, pitch=1.0):
                 except Exception:
                     try: os.unlink(out.name)
                     except Exception: pass
-            loop.close()
+            
             if len(files) >= len(chunks) * 0.8:
                 result = _build_audio(files, speed, pitch)
                 if result:
                     return result
         except Exception:
             pass
+    
     # fallback to gTTS
     try:
         from gtts import gTTS
@@ -313,17 +333,17 @@ def make_audio(text, speed=1.0, pitch=1.0):
     return text_to_speech_full(text, speed, pitch)
 
 # =============================================================================
-# DOCUMENT GENERATION (short versions for demo)
+# DOCUMENT GENERATION
 # =============================================================================
 def generate_dummy(r1, r2, r3, r4, typ: str):
     if not r1:
         return "Run analysis first."
     prompt_map = {
-        "letter": "Draft a formal Letter Before Action...",
-        "complaint": "Draft a formal regulatory complaint...",
-        "witness": "Draft a Witness Statement...",
-        "skeleton": "Draft a Skeleton Argument...",
-        "referral": "Draft a Regulatory Referral..."
+        "letter": "Draft a formal Letter Before Action to the relevant institution naming the breach, citing statute, and demanding remedy within 14 days.",
+        "complaint": "Draft a formal regulatory complaint to the appropriate ombudsman or regulator, stating facts, breaches, and requested remedy.",
+        "witness": "Draft a Witness Statement in first person, confirming facts, dates, and personal knowledge of the breaches.",
+        "skeleton": "Draft a Skeleton Argument for court proceedings: issues in dispute, legal principles, case law, and conclusions.",
+        "referral": "Draft a Regulatory Referral setting out the systemic breaches and requesting investigation and enforcement action."
     }
     prompt = prompt_map.get(typ, "")
     return call_llama(
@@ -396,6 +416,9 @@ with gr.Blocks(css=CSS, title="Metabolic Phoenix Framework") as demo:
         <p class="own">Invented &amp; Owned by Dwayne Anthony Brian Galloway &nbsp;·&nbsp; 2026</p>
     </div>""")
 
+    # Analysis state to persist across cycles
+    analysis_state = gr.State([None, None, None, None])
+
     pdf_input = gr.File(
         label="Upload Documents — PDF · Image · Audio · Video · Text — drop multiple",
         file_types=[
@@ -433,15 +456,15 @@ with gr.Blocks(css=CSS, title="Metabolic Phoenix Framework") as demo:
         gen_referral = gr.Button("Regulatory Referral", variant="secondary")
         doc_output = gr.Textbox(label="Generated Document", lines=20)
 
-    # Helper to bind analysis outputs for generation
-    def _get_outputs(r1, r2, r3, r4):
-        return r1, r2, r3, r4
+    def analyse_and_update_state(files):
+        r1, r2, r3, r4 = analyse(files)
+        return r1, r2, r3, r4, [r1, r2, r3, r4]
 
-    # Analyse
+    # Analyse button: run analysis and update state
     analyse_btn.click(
-        fn=analyse,
+        fn=analyse_and_update_state,
         inputs=pdf_input,
-        outputs=[out1, out2, out3, out4]
+        outputs=[out1, out2, out3, out4, analysis_state]
     )
 
     # TTS for each cycle
@@ -452,20 +475,15 @@ with gr.Blocks(css=CSS, title="Metabolic Phoenix Framework") as demo:
 
     # Document reading without analysis
     read_doc_btn.click(
-        fn=lambda files, speed, pitch: text_to_speech_full(
-            "\n\n".join([extract_content(f) for f in (files if isinstance(files, list) else [files])]),
-            speed, pitch
+        fn=lambda files: text_to_speech_full(
+            "\n\n".join([extract_content(f) for f in (files if isinstance(files, list) else [files] if files else [])]),
+            1.0, 1.0
         ),
-        inputs=[pdf_input, gr.Slider(0.5, 2.0, value=1.0, label="Speed"),
-                gr.Slider(0.5, 2.0, value=1.0, label="Pitch")],
+        inputs=pdf_input,
         outputs=read_doc_audio
     )
 
-    # Document generation (bind analysis outputs as state)
-    analysis_state = gr.State([None, None, None, None])
-    for out in [out1, out2, out3, out4]:
-        out.change(lambda *vals: vals, [out1, out2, out3, out4], analysis_state)
-
+    # Document generation (use analysis_state)
     gen_letter.click(fn=lambda s: generate_letter(*s), inputs=analysis_state, outputs=doc_output)
     gen_complaint.click(fn=lambda s: generate_complaint(*s), inputs=analysis_state, outputs=doc_output)
     gen_witness.click(fn=lambda s: generate_witness(*s), inputs=analysis_state, outputs=doc_output)
@@ -477,4 +495,4 @@ with gr.Blocks(css=CSS, title="Metabolic Phoenix Framework") as demo:
     </div>""")
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()