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Update app.py
Browse files
app.py
CHANGED
@@ -13,8 +13,8 @@ headers = {
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# Rate limiting configuration
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request_times = defaultdict(list)
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MAX_REQUESTS =
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TIME_WINDOW =
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def get_real_ip(request: gr.Request):
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"""Extract real IP address using x-forwarded-for header or fallback"""
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@@ -35,10 +35,7 @@ def check_rate_limit(request: gr.Request):
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ip = get_real_ip(request)
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now = time.time()
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print(f"Ip: {ip}")
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print(f"Now: {now}")
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print(f"Request times: {request_times[ip]}")
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# Clean up old timestamps outside the time window
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request_times[ip] = [t for t in request_times[ip] if now - t < TIME_WINDOW]
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@@ -54,9 +51,9 @@ def check_rate_limit(request: gr.Request):
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return True, ""
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def transcribe_audio(input_type, audio_url, file_store_key, language):
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"""Transcribe audio using JigsawStack Speech-to-Text API"""
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# Check rate limit first
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rate_limit_ok, rate_limit_msg = check_rate_limit(request)
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if not rate_limit_ok:
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@@ -81,6 +78,7 @@ def transcribe_audio(input_type, audio_url, file_store_key, language):
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)
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response.raise_for_status()
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result = response.json()
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if not result.get("success"):
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error_msg = f"Error: API call failed - {result.get('message', 'Unknown error')}"
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return error_msg, ""
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@@ -97,6 +95,7 @@ with gr.Blocks() as demo:
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<h1 style='font-size:2.2em; margin-bottom: 0.2em;'>Speech-to-Text Transcription</h1>
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<p style='font-size:1.2em; margin-top: 0;'>Transcribe video and audio files with ease leveraging Whisper large V3 AI model.</p>
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<p style='font-size:1em; margin-top: 0.5em;'>Supported formats: MP3, WAV, M4A, FLAC, AAC, OGG, WEBM. Max file size: 100MB, Max duration: 4 hours.</p>
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</div>
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""")
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with gr.Row():
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# Rate limiting configuration
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request_times = defaultdict(list)
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MAX_REQUESTS = 20 # Maximum requests per time window
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TIME_WINDOW = 3600 # Time window in seconds (1 hour)
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def get_real_ip(request: gr.Request):
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"""Extract real IP address using x-forwarded-for header or fallback"""
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ip = get_real_ip(request)
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now = time.time()
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# Clean up old timestamps outside the time window
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request_times[ip] = [t for t in request_times[ip] if now - t < TIME_WINDOW]
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return True, ""
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def transcribe_audio(input_type, audio_url, file_store_key, language, request: gr.Request):
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"""Transcribe audio using JigsawStack Speech-to-Text API"""
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# Check rate limit first
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rate_limit_ok, rate_limit_msg = check_rate_limit(request)
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if not rate_limit_ok:
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)
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response.raise_for_status()
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result = response.json()
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if not result.get("success"):
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error_msg = f"Error: API call failed - {result.get('message', 'Unknown error')}"
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return error_msg, ""
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<h1 style='font-size:2.2em; margin-bottom: 0.2em;'>Speech-to-Text Transcription</h1>
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<p style='font-size:1.2em; margin-top: 0;'>Transcribe video and audio files with ease leveraging Whisper large V3 AI model.</p>
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<p style='font-size:1em; margin-top: 0.5em;'>Supported formats: MP3, WAV, M4A, FLAC, AAC, OGG, WEBM. Max file size: 100MB, Max duration: 4 hours.</p>
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<p style='font-size:0.9em; margin-top: 0.5em; color: #666;'>Rate limit: {MAX_REQUESTS} requests per {TIME_WINDOW} seconds per IP</p>
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</div>
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""")
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with gr.Row():
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