Update app.py

app.py

@@ -6,6 +6,7 @@ import torch
 import json
 import urllib.parse
 import soundfile as sf
+import time 
 
 # Fetch Hugging Face API Token securely from environment variables
 HF_API_TOKEN = os.getenv("HF")  # This fetches the token securely
@@ -22,12 +23,38 @@ with open("qa_dataset.json", "r", encoding="utf-8") as f:
     qa_data = json.load(f)
 
 # Function to transcribe audio using Whisper
+def wait_for_model_ready(model_url, headers, timeout=300):
+    start_time = time.time()
+    while time.time() - start_time < timeout:
+        # Send a "dummy" GET request to check status
+        response = requests.get(model_url, headers=headers)
+        result = response.json()
+
+        if not ("error" in result and "loading" in result["error"].lower()):
+            print("✅ Model is ready!")
+            return True
+
+        print("⏳ Model is still loading, waiting 10 seconds...")
+        time.sleep(10)
+
+    print("❌ Model did not become ready in time.")
+    return False  # timeout
+
 def transcribe_audio(audio_file):
     headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+
+    # Wait for Whisper model to be ready
+    if not wait_for_model_ready(WHISPER_API_URL, headers):
+        return "Error: Whisper model did not load in time. Please try again later."
+
+    # Now send the audio after model is ready
     with open(audio_file, "rb") as f:
         response = requests.post(WHISPER_API_URL, headers=headers, data=f)
-    print(response.json())  # Add this to log the entire response
-    return response.json().get("text", "Error: No transcription text returned.")
+
+    result = response.json()
+    print(result)  # log response
+
+    return result.get("text", "Error: No transcription text returned.")
 
 # Function to generate TTS audio URL (Google Translate API for Tamil Voice)
 def get_tts_audio_url(text, lang="ta"):