Upload app.py

app.py

@@ -6,16 +6,7 @@ import asyncio
 import subprocess
 from pathlib import Path
 import gradio as gr
-
-try:
-    import spaces
-    SPACES_ENABLED = True
-except ImportError:
-    SPACES_ENABLED = False
-    class spaces:
-        @staticmethod
-        def GPU(f):
-            return f
+import spaces
 
 # All dependencies are installed via requirements.txt / apt.txt
 from pypdf import PdfReader
@@ -39,59 +30,43 @@ _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 {}
-    
-    # Add default header to prevent gating issues
-    headers["User-Agent"] = "Phoenix-Framework/1.0"
-    
     for model in _MODELS:
-        for attempt in range(5):
+        for attempt in range(4):
             try:
+                fmt = prompt
                 resp = _req.post(
                     _FREE_API.format(model=model),
                     headers=headers,
                     json={
-                        "inputs": prompt,
+                        "inputs": fmt,
                         "parameters": {
                             "max_new_tokens": max_tokens,
                             "temperature": 0.7,
                             "return_full_text": False,
                             "do_sample": True,
                         },
-                        "options": {"wait_for_model": True, "use_cache": False}
+                        "options": {"wait_for_model": True}
                     },
                     timeout=180
                 )
-                
                 if resp.status_code == 200:
                     result = resp.json()
                     if isinstance(result, list) and result:
                         text = result[0].get("generated_text", "").strip()
                         if text:
                             return text
-                    elif isinstance(result, dict) and "generated_text" in result:
-                        text = result.get("generated_text", "").strip()
-                        if text:
-                            return text
-                
-                # 503 = model loading, retry with backoff
-                if resp.status_code == 503:
-                    wait_time = min(60 * (attempt + 1), 300)  # cap at 5 min
-                    time.sleep(wait_time)
+                elif resp.status_code == 503:
+                    time.sleep(30 * (attempt + 1))
                     continue
-                
-                # 429/402 = rate limit or payment issue, skip model
-                if resp.status_code in [429, 402]:
+                elif resp.status_code in [429, 402]:
                     break
-                
-                # Other errors, retry after delay
-                time.sleep(5 * (attempt + 1))
-                continue
-                
-            except Exception as e:
-                time.sleep(5 * (attempt + 1))
+                else:
+                    time.sleep(10)
+                    continue
+            except Exception:
+                time.sleep(10)
                 continue
-    
-    return "Could not reach AI models. Please check: 1) HF_TOKEN env var is set, 2) Network connection, 3) Hugging Face API status (try again in 60 seconds)."
+    return "Could not reach AI. Models may be loading — please try again in 30 seconds."
 
 # =============================================================================
 # FILE EXTRACTION
@@ -116,7 +91,6 @@ def extract_image_text(path):
     except Exception as e:
         return f"[Image OCR error: {e}]"
 
-@spaces.GPU
 def transcribe_audio(path):
     try:
         model = whisper.load_model("base")
@@ -130,7 +104,6 @@ def extract_content(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"]:
@@ -223,8 +196,9 @@ 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\nBreach Analysis:\n{r2[:1500]}\n\n"
-        f"Loop Output:\n{r3[:1000]}\n\nCase Summary Context Complete\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"{'='*60}\n\n"
         "Produce a complete litigation-ready case summary. "
         "Breaches have occurred and are on the record. Write accordingly.\n\n"
@@ -233,8 +207,7 @@ 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,
-        max_tokens=2500
+        "Ms. Nala | Injectiv8 | Dwayne Anthony Brian Galloway" + PLAIN_ENGLISH
     )
     return r1, r2, r3, r4
 
@@ -316,7 +289,6 @@ def text_to_speech_full(text, speed=1.0, pitch=1.0):
                     return result
         except Exception:
             pass
-    
     # fallback to gTTS
     try:
         from gtts import gTTS
@@ -341,17 +313,17 @@ def make_audio(text, speed=1.0, pitch=1.0):
     return text_to_speech_full(text, speed, pitch)
 
 # =============================================================================
-# DOCUMENT GENERATION
+# DOCUMENT GENERATION (short versions for demo)
 # =============================================================================
 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 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."
+        "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..."
     }
     prompt = prompt_map.get(typ, "")
     return call_llama(
@@ -424,9 +396,6 @@ 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=[
@@ -464,15 +433,15 @@ 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)
 
-    def analyse_and_update_state(files):
-        r1, r2, r3, r4 = analyse(files)
-        return r1, r2, r3, r4, [r1, r2, r3, r4]
+    # Helper to bind analysis outputs for generation
+    def _get_outputs(r1, r2, r3, r4):
+        return r1, r2, r3, r4
 
-    # Analyse button: run analysis and update state
+    # Analyse
     analyse_btn.click(
-        fn=analyse_and_update_state,
+        fn=analyse,
         inputs=pdf_input,
-        outputs=[out1, out2, out3, out4, analysis_state]
+        outputs=[out1, out2, out3, out4]
     )
 
     # TTS for each cycle
@@ -483,15 +452,20 @@ with gr.Blocks(css=CSS, title="Metabolic Phoenix Framework") as demo:
 
     # Document reading without analysis
     read_doc_btn.click(
-        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
+        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
         ),
-        inputs=pdf_input,
+        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")],
         outputs=read_doc_audio
     )
 
-    # Document generation (use analysis_state)
+    # 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)
+
     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)
@@ -499,13 +473,8 @@ with gr.Blocks(css=CSS, title="Metabolic Phoenix Framework") as demo:
     gen_referral.click(fn=lambda s: generate_referral(*s), inputs=analysis_state, outputs=doc_output)
 
     gr.HTML("""<div class="foot">
-        Metabolic Phoenix Framework · Dwayne Anthony Brian Galloway · 2026
+        Metabolic Phoenix Framework &nbsp;·&nbsp; Dwayne Anthony Brian Galloway &nbsp;·&nbsp; 2026
     </div>""")
 
 if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True,
-        share=False  # Spaces handles sharing automatically
-    )
+    demo.launch()