Update app.py

app.py

@@ -14,8 +14,11 @@ RUNS.mkdir(exist_ok=True)
 # ----------------- Logging -----------------
 def append_log(msg: str):
     msg = (msg or "").rstrip("\n")
-    with open(LOG, "a", encoding="utf-8") as lf:
-        lf.write(msg + "\n")
+    try:
+        with open(LOG, "a", encoding="utf-8") as lf:
+            lf.write(msg + "\n")
+    except Exception:
+        pass
 
 def read_logs():
     return LOG.read_text(encoding="utf-8")[-20000:] if LOG.exists() else "⏳ Waiting…"
@@ -49,6 +52,7 @@ def dropdown_update_safe(models, prefer=None):
 
 # ----------------- Dataset Upload -----------------
 def upload_dataset(file):
+    append_log("📥 upload_dataset clicked")
     if not file:
         return "❌ No file selected.", ls_workspace()
     if hasattr(file, "name") and os.path.isfile(file.name):
@@ -58,10 +62,7 @@ def upload_dataset(file):
 
 # ----------------- Training (Live Logs) -----------------
 def start_training_live(run_name):
-    """
-    Streams training logs to the UI while the subprocess runs.
-    Yields tuples for outputs: [status, download_file, workspace, logs, model_dropdown]
-    """
+    append_log("🚀 start_training_live clicked")
     # Quick guard: dataset must exist
     if not DATA.exists():
         msg = "❌ dataset.jsonl not found. Upload a JSONL dataset first."
@@ -124,9 +125,7 @@ def start_training_live(run_name):
         if line:
             append_log(line.rstrip("\n"))
             live_log.write(line)
-            # Trim to last ~20k chars for UI
             text = live_log.getvalue()[-20000:]
-            # yield with download hidden (until zip exists)
             yield (
                 status_msg,
                 gr.update(value=None, visible=False),
@@ -134,7 +133,6 @@ def start_training_live(run_name):
                 text,
                 dropdown_update_safe(list_models(), prefer=None),
             )
-            # if zip appears during training (e.g., early save), surface it
             if zip_path.exists():
                 yield (
                     "📦 Model zip created during run.",
@@ -160,6 +158,7 @@ def start_training_live(run_name):
         yield (info, gr.update(value=None, visible=False), ls_workspace(), final_logs, model_update)
 
 def refresh_download():
+    append_log("↻ refresh_download clicked")
     zips = sorted(RUNS.glob("*.zip"), key=lambda p: p.stat().st_mtime, reverse=True)
     latest = zips[0] if zips else None
     models = list_models()
@@ -171,6 +170,7 @@ def refresh_download():
 
 # ----------------- Import a Zip as Model Folder -----------------
 def import_zip(zfile):
+    append_log("📦 import_zip clicked")
     if not zfile:
         return "❌ No zip selected.", list_models()
     dest = ROOT / "imported_model"
@@ -222,6 +222,7 @@ def ping():
     return "✅ UI is connected and responding."
 
 def load_selected_model(model_path):
+    append_log("📦 load_selected_model clicked")
     # Dropdown may pass a list; coerce to string
     if isinstance(model_path, list):
         model_path = model_path[0] if model_path else None
@@ -242,11 +243,56 @@ def load_selected_model(model_path):
         append_log("❌ Load error:\n" + tb)
         return "❌ Error while loading model:\n" + "".join(traceback.format_exception_only(type(e), e))
 
+def generate_once(model_path, prompt):
+    """Non-streaming fallback."""
+    append_log("▶ generate_once clicked")
+    # Coerce
+    if isinstance(model_path, list):
+        model_path = model_path[0] if model_path else None
+
+    # validate
+    if not model_path:
+        msg = "❌ Select a model from the dropdown first."
+        append_log(msg); return msg
+    if not isinstance(model_path, str):
+        msg = f"❌ Invalid model path type: {type(model_path)._name_}"
+        append_log(msg); return msg
+    if not Path(model_path).exists():
+        msg = f"❌ Model folder not found: {model_path}"
+        append_log(msg); return msg
+    if not prompt or not prompt.strip():
+        msg = "❌ Enter a prompt."
+        append_log(msg); return msg
+
+    try:
+        pipe = get_generation_pipeline(model_path)
+        append_log(f"📝 Generating once… prompt_len={len(prompt)}")
+        result = pipe(
+            prompt.strip(),
+            max_new_tokens=80,
+            do_sample=True,
+            temperature=0.3,
+            top_p=0.9,
+            repetition_penalty=1.15,
+            no_repeat_ngram_size=4,
+            truncation=True,
+            return_full_text=True,
+        )
+        text = result[0].get("generated_text", "")
+        if not text:
+            append_log("⚠ Empty generated_text")
+            return "⚠ Model returned empty text. Try lowering temperature or adding more context."
+        append_log("✅ Generation OK.")
+        return text
+    except Exception as e:
+        tb = traceback.format_exc()
+        append_log("❌ Generation error:\n" + tb)
+        return "❌ Error during generation:\n" + "".join(traceback.format_exception_only(type(e), e))
+
 def generate_stream(model_path, prompt):
-    """Stream intermediate status to prove the button is working, then final text."""
-    # immediate feedback
+    """Streaming version (if Frontend streaming works)."""
     yield "⏳ Loading model…"
-    append_log("▶ Generate clicked")
+    append_log("▶ generate_stream clicked")
 
     # Coerce
     if isinstance(model_path, list):
@@ -269,10 +315,10 @@ def generate_stream(model_path, prompt):
     try:
         pipe = get_generation_pipeline(model_path)
         yield "⚙ Generating… (this may take a bit on CPU)"
-        append_log(f"📝 Generating… prompt_len={len(prompt)}")
+        append_log(f"📝 Generating (stream)… prompt_len={len(prompt)}")
         result = pipe(
             prompt.strip(),
-            max_new_tokens=80,      # quicker to show something
+            max_new_tokens=80,
             do_sample=True,
             temperature=0.3,
             top_p=0.9,
@@ -302,13 +348,13 @@ with gr.Blocks(title="Python AI — Train & Test") as app:
         gr.Markdown("### Choose a model folder or upload a .zip, then prompt it")
         with gr.Row():
             refresh_btn = gr.Button("↻ Refresh Model List")
-            ping_btn = gr.Button("🔔 Ping UI")  # quick sanity check
+            ping_btn = gr.Button("🔔 Ping UI")  # sanity check
         model_list = gr.Dropdown(
             choices=list_models(),
             label="Available AIs",
             interactive=True,
-            allow_custom_value=True,  # keeps UI quiet when empty
-            multiselect=False         # force single selection
+            allow_custom_value=True,
+            multiselect=False
         )
         load_btn = gr.Button("📦 Load Model")
         load_status = gr.Textbox(label="Model Status", interactive=False)
@@ -321,7 +367,9 @@ with gr.Blocks(title="Python AI — Train & Test") as app:
             lines=8,
             placeholder="### Instruction:\nPython: write a function ...\n### Response:\n"
         )
-        go = gr.Button("Generate")
+        with gr.Row():
+            go_stream = gr.Button("Generate (stream)")
+            go_once   = gr.Button("Generate (once)")
         out = gr.Textbox(label="AI Response", lines=20)
 
     # ---------- Train Tab ----------
@@ -338,29 +386,28 @@ with gr.Blocks(title="Python AI — Train & Test") as app:
         refresh_dl_btn = gr.Button("Refresh Download")
 
     # ---------- Wiring ----------
-    # Upload + workspace
     ds.change(upload_dataset, inputs=ds, outputs=[up_status, ws])
 
-    # Train (live streaming)
     start.click(
         start_training_live,
         inputs=[run_name],
         outputs=[status, download_file, ws, logs, model_list]
     )
 
-    # Download refresh
     refresh_dl_btn.click(
         refresh_download,
         outputs=[download_file, ws, model_list]
     )
 
-    # Test tab helpers
     refresh_btn.click(lambda: dropdown_update_safe(list_models()), outputs=model_list)
     ping_btn.click(ping, outputs=out)
     load_btn.click(load_selected_model, inputs=[model_list], outputs=[load_status])
     zip_in.change(import_zip, inputs=zip_in, outputs=[import_status, model_list])
 
-    # Streamed generation output
-    go.click(generate_stream, inputs=[model_list, prompt], outputs=out)
+    # Generation (two modes)
+    go_stream.click(generate_stream, inputs=[model_list, prompt], outputs=out)
+    go_once.click(generate_once, inputs=[model_list, prompt], outputs=out)
 
-app.queue(default_concurrency_limit=1).launch()
+# Critical: disable SSR; ensure queue is enabled
+app.queue(default_concurrency_limit=1)
+app.launch(ssr_mode=False, show_error=True)