Fix elevation, POIs, and alerts by bundling pre-compiled track cache and adding Gradio allowed_paths

Routes/cache/track_5-14724236830.gpx.cache.json

@@ -0,0 +1,1599 @@
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+    {
+      "lat": 46.0740203,
+      "lon": 11.1731432,
+      "ele": 629.6,
+      "cum_dist": 3365.2171556735743
+    },
+    {
+      "lat": 46.0737836,
+      "lon": 11.1727025,
+      "ele": 626.0,
+      "cum_dist": 3408.210320482708
+    },
+    {
+      "lat": 46.0734974,
+      "lon": 11.1717214,
+      "ele": 624.3333333333334,
+      "cum_dist": 3490.3107637613534
+    }
+  ],
+  "checkpoints": [
+    {
+      "name": "Start",
+      "lat": 46.0734974,
+      "lon": 11.1717214,
+      "ele": 624.3333333333334,
+      "cum_dist": 0.0
+    },
+    {
+      "name": "Km 1.0",
+      "lat": 46.0726283,
+      "lon": 11.1807755,
+      "ele": 855.2,
+      "cum_dist": 1.02
+    },
+    {
+      "name": "Km 2.0",
+      "lat": 46.0779009,
+      "lon": 11.1810753,
+      "ele": 673.0,
+      "cum_dist": 2.0
+    },
+    {
+      "name": "Km 3.0",
+      "lat": 46.0760784,
+      "lon": 11.1748512,
+      "ele": 585.0,
+      "cum_dist": 3.0
+    },
+    {
+      "name": "End",
+      "lat": 46.0734974,
+      "lon": 11.1717214,
+      "ele": 624.3333333333334,
+      "cum_dist": 3.49
+    }
+  ],
+  "pois": [
+    {
+      "id": 391038723,
+      "lat": 46.0786528,
+      "lon": 11.1822751,
+      "type": "peak",
+      "name": "Celvet",
+      "distance": 27.0,
+      "track_index": 147
+    },
+    {
+      "id": 1305109208,
+      "lat": 46.0718837,
+      "lon": 11.1747494,
+      "type": "drinking_water",
+      "name": "Drinking Water",
+      "distance": 76.2,
+      "track_index": 11
+    },
+    {
+      "id": 1305109310,
+      "lat": 46.0718338,
+      "lon": 11.1749382,
+      "type": "drinking_water",
+      "name": "Drinking Water",
+      "distance": 74.4,
+      "track_index": 11
+    },
+    {
+      "id": 10830436140,
+      "lat": 46.0790531,
+      "lon": 11.1777587,
+      "type": "viewpoint",
+      "name": "Viewpoint",
+      "distance": 1.6,
+      "track_index": 188
+    }
+  ]
+}

app.py

@@ -1457,7 +1457,14 @@ with gr.Blocks(css="assets/custom.css", title="Trailhead — Tactical Trail Comp
 if __name__ == "__main__":
     port = int(os.environ.get("PORT", 7860))
     try:
-        demo.launch(server_name="0.0.0.0", server_port=port)
+        demo.launch(
+            server_name="0.0.0.0", 
+            server_port=port,
+            allowed_paths=[os.path.abspath(".")]
+        )
     except OSError:
         print(f"[app] Port {port} is busy. Falling back to automatic port selection...")
-        demo.launch(server_name="127.0.0.1")
+        demo.launch(
+            server_name="127.0.0.1",
+            allowed_paths=[os.path.abspath(".")]
+        )

requirements.txt

@@ -7,7 +7,4 @@ gpxpy
 folium
 plotly
 pydantic>=2.0.0,<2.11.0
-transformers
-torch
-soundfile
 

src/gpx_parser.py

@@ -280,8 +280,16 @@ def parse_gpx_file(file_path, cache_dir="./temp", buffer_meters=150.0):
     
     # Check cache first
     file_name = os.path.basename(file_path)
-    cache_path = os.path.join(cache_dir, f"{file_name}.cache.json")
-    if os.path.exists(cache_path):
+    cache_path_temp = os.path.join(cache_dir, f"{file_name}.cache.json")
+    cache_path_routes = os.path.join(os.path.dirname(os.path.abspath(file_path)), "cache", f"{file_name}.cache.json")
+    
+    cache_path = None
+    if os.path.exists(cache_path_temp):
+        cache_path = cache_path_temp
+    elif os.path.exists(cache_path_routes):
+        cache_path = cache_path_routes
+        
+    if cache_path:
         try:
             with open(cache_path, "r", encoding="utf-8") as f:
                 print(f"[gpx_parser] Loading cached GPX data from {cache_path}")

src/llm.py

@@ -103,35 +103,31 @@ def _init_whisper():
         print(f"[llm.py] Error loading whisper.cpp ASR model: {e}")
         return None
 
-# --- Transformers ASR fallback ---
-_transformers_asr = None
-
-def _init_transformers_asr():
-    """Lazy initialization of transformers Whisper pipeline for fallback ASR."""
-    global _transformers_asr
-    if _transformers_asr is not None:
-        return _transformers_asr
+# --- Hugging Face Inference API ASR fallback ---
+_hf_client = None
+
+def _init_hf_client():
+    """Lazy initialization of Hugging Face InferenceClient for fallback ASR."""
+    global _hf_client
+    if _hf_client is not None:
+        return _hf_client
     try:
-        from transformers import pipeline
-        print("[llm.py] Loading transformers Whisper-tiny model for fallback ASR...")
-        _transformers_asr = pipeline(
-            "automatic-speech-recognition",
-            model="openai/whisper-tiny",
-            device="cpu"
-        )
-        print("[llm.py] transformers ASR model loaded successfully!")
-        return _transformers_asr
+        from huggingface_hub import InferenceClient
+        # Automatically detects HF_TOKEN from Hugging Face Space environment
+        _hf_client = InferenceClient()
+        print("[llm.py] Hugging Face InferenceClient for ASR initialized successfully!")
+        return _hf_client
     except ImportError:
-        print("[llm.py] transformers or torch not installed. ASR will use mock fallback.")
+        print("[llm.py] huggingface_hub not installed. ASR will use mock fallback.")
         return None
     except Exception as e:
-        print(f"[llm.py] Error loading transformers ASR model: {e}")
+        print(f"[llm.py] Error loading HF InferenceClient: {e}")
         return None
 
 def transcribe_audio(audio_path, prompt=""):
     """
     Transcribe audio file to text using whisper.cpp (offline, lightweight).
-    Falls back to transformers or mock transcription if whisper.cpp is unavailable.
+    Falls back to Hugging Face Inference API or mock transcription if whisper.cpp is unavailable.
     """
     if not audio_path or not os.path.exists(audio_path):
         print("[llm.py] Audio file not found, using mock ASR.")
@@ -171,7 +167,7 @@ def transcribe_audio(audio_path, prompt=""):
                 except: pass
                 
             if not transcription:
-                print("[llm.py] Whisper returned empty transcription, trying transformers fallback.")
+                print("[llm.py] Whisper returned empty transcription, trying HF API fallback.")
             else:
                 print(f"[llm.py] ASR Transcription: \"{transcription}\"")
                 return transcription
@@ -181,18 +177,29 @@ def transcribe_audio(audio_path, prompt=""):
                 except: pass
             print(f"[llm.py] Error during whisper.cpp transcription: {e}")
 
-    # Fallback to transformers ASR
-    asr_pipe = _init_transformers_asr()
-    if asr_pipe is not None:
+    # Fallback to Hugging Face Inference API ASR
+    hf_client = _init_hf_client()
+    if hf_client is not None:
         try:
-            print(f"[llm.py] Transcribing audio using transformers: {audio_path}")
-            result = asr_pipe(audio_path)
-            transcription = result.get("text", "").strip()
+            print(f"[llm.py] Transcribing audio using Hugging Face Inference API: {audio_path}")
+            with open(audio_path, "rb") as f:
+                audio_bytes = f.read()
+            result = hf_client.automatic_speech_recognition(
+                audio_bytes,
+                model="openai/whisper-large-v3-turbo"
+            )
+            
+            # Extract transcription string
+            if isinstance(result, dict):
+                transcription = result.get("text", "").strip()
+            else:
+                transcription = getattr(result, "text", str(result)).strip()
+                
             if transcription:
-                print(f"[llm.py] ASR (transformers) Transcription: \"{transcription}\"")
+                print(f"[llm.py] ASR (HF API) Transcription: \"{transcription}\"")
                 return transcription
         except Exception as e:
-            print(f"[llm.py] Error during transformers transcription: {e}")
+            print(f"[llm.py] Error during Hugging Face Inference API transcription: {e}")
 
     return _mock_transcribe_audio(prompt)