M4 notifications

ai_scan.py

@@ -0,0 +1,146 @@
+"""
+AI detection scanner using HuggingFace Inference API (free).
+Model: Hello-SimpleAI/chatgpt-detector-roberta
+
+Usage:
+    python ai_scan.py <hf_token> <folder> [folder2 ...]
+
+Example:
+    python ai_scan.py hf_abc123 lib/features/messaging lib/features/settings
+
+Get a free token at: huggingface.co/settings/tokens
+"""
+
+import sys
+import time
+import requests
+from pathlib import Path
+
+API_URL = "https://api-inference.huggingface.co/models/Hello-SimpleAI/chatgpt-detector-roberta"
+EXTENSIONS = {".dart", ".kt", ".swift", ".java"}
+MAX_CHARS = 512    # RoBERTa token limit (keep to ~500 chars to be safe)
+RATE_DELAY = 1.5   # seconds between requests
+
+
+def scan_file(api_key: str, file_path: Path) -> float | None:
+    text = file_path.read_text(encoding="utf-8", errors="ignore").strip()
+    if len(text) < 80:
+        return None  # too short to be meaningful
+
+    # Truncate to model limit
+    if len(text) > MAX_CHARS:
+        text = text[:MAX_CHARS]
+
+    try:
+        resp = requests.post(
+            API_URL,
+            headers={"Authorization": f"Bearer {api_key}"},
+            json={"inputs": text},
+            timeout=30,
+        )
+        if resp.status_code == 503:
+            # Model is loading — wait and retry once
+            print("  model loading, waiting 20s...")
+            time.sleep(20)
+            resp = requests.post(
+                API_URL,
+                headers={"Authorization": f"Bearer {api_key}"},
+                json={"inputs": text},
+                timeout=30,
+            )
+        resp.raise_for_status()
+
+        # Response: [[{"label": "ChatGPT", "score": 0.87}, {"label": "Human", "score": 0.13}]]
+        labels = resp.json()[0]
+        ai_score = next((x["score"] for x in labels if x["label"].lower() != "human"), None)
+        if ai_score is None:
+            # fallback: 1 - human score
+            human = next((x["score"] for x in labels if x["label"].lower() == "human"), 0)
+            ai_score = 1 - human
+        return round(ai_score * 100, 1)
+
+    except requests.HTTPError as e:
+        if resp.status_code == 429:
+            print("  rate limited — waiting 60s...")
+            time.sleep(60)
+        else:
+            print(f"  HTTP {resp.status_code}: {e}")
+        return None
+    except Exception as e:
+        print(f"  error: {e}")
+        return None
+
+
+def scan_folder(api_key: str, folder: str) -> list[tuple[str, float]]:
+    root = Path(folder)
+    if not root.exists():
+        print(f"Folder not found: {folder}")
+        return []
+
+    files = sorted(
+        f for f in root.rglob("*") if f.suffix in EXTENSIONS and f.is_file()
+    )
+    if not files:
+        print(f"No supported files found in {folder}")
+        return []
+
+    print(f"\nScanning {len(files)} files in '{folder}'\n")
+    col = 68
+    print(f"{'File':<{col}} {'AI %':>6}")
+    print("─" * (col + 8))
+
+    results = []
+    for i, fp in enumerate(files):
+        label = str(fp.relative_to(root.parent))
+        pct = scan_file(api_key, fp)
+
+        if pct is not None:
+            results.append((label, pct))
+            print(f"{label:<{col}} {pct:>5.1f}%")
+        else:
+            print(f"{label:<{col}} {'skip':>6}")
+
+        if i < len(files) - 1:
+            time.sleep(RATE_DELAY)
+
+    return results
+
+
+def main():
+    if len(sys.argv) < 3:
+        print(__doc__)
+        sys.exit(1)
+
+    api_key = sys.argv[1]
+    folders = sys.argv[2:]
+
+    all_results: list[tuple[str, float]] = []
+    for folder in folders:
+        all_results.extend(scan_folder(api_key, folder))
+
+    if not all_results:
+        print("Nothing scanned.")
+        return
+
+    col = 68
+    print("\n" + "═" * (col + 8))
+    print(f"{'SUMMARY':<{col}}")
+    print("═" * (col + 8))
+
+    all_results.sort(key=lambda x: x[1], reverse=True)
+    for label, pct in all_results:
+        bar = "█" * int(pct / 5)
+        print(f"{label:<{col}} {pct:>5.1f}%  {bar}")
+
+    avg = sum(p for _, p in all_results) / len(all_results)
+    print("─" * (col + 8))
+    print(f"\n  Overall average : {avg:.1f}% AI")
+    print(f"  Files scanned   : {len(all_results)}")
+    highest = max(all_results, key=lambda x: x[1])
+    lowest = min(all_results, key=lambda x: x[1])
+    print(f"  Highest         : {highest[1]:.1f}%  ({highest[0]})")
+    print(f"  Lowest          : {lowest[1]:.1f}%  ({lowest[0]})\n")
+
+
+if __name__ == "__main__":
+    main()

android/app/build.gradle.kts

@@ -78,4 +78,5 @@ flutter {
 
 dependencies {
     coreLibraryDesugaring("com.android.tools:desugar_jdk_libs:2.1.4")
+    implementation("org.jetbrains.kotlinx:kotlinx-coroutines-android:1.7.3")
 }

android/app/src/main/AndroidManifest.xml

@@ -70,6 +70,33 @@
             </intent-filter>
         </receiver>
 
+        <!-- Home screen player widget -->
+        <receiver
+            android:name=".PlayerWidget"
+            android:exported="true">
+            <intent-filter>
+                <action android:name="android.appwidget.action.APPWIDGET_UPDATE" />
+                <action android:name="com.example.rythmify.WIDGET_PREV" />
+                <action android:name="com.example.rythmify.WIDGET_PLAY_PAUSE" />
+                <action android:name="com.example.rythmify.WIDGET_NEXT" />
+            </intent-filter>
+            <meta-data
+                android:name="android.appwidget.provider"
+                android:resource="@xml/player_widget_info" />
+        </receiver>
+
+        <!-- Home screen likes widget -->
+        <receiver
+            android:name=".LikesWidget"
+            android:exported="true">
+            <intent-filter>
+                <action android:name="android.appwidget.action.APPWIDGET_UPDATE" />
+            </intent-filter>
+            <meta-data
+                android:name="android.appwidget.provider"
+                android:resource="@xml/likes_widget_info" />
+        </receiver>
+
         <meta-data
             android:name="flutterEmbedding"
             android:value="2" />