save

app.py

@@ -1,13 +1,11 @@
 import os
 os.system("pip install git+https://github.com/openai/whisper.git")
-import gradio as gr
+
 import whisper
 from flask import Flask, jsonify, request
 import requests
-import streamlit as st
 import time
 
-
 model = whisper.load_model("large-v2")
 
 app = Flask(__name__)
@@ -22,24 +20,6 @@ def runApi():
     start_time = time.time()
 
     audio_url = request.form.get("audio_url")
-    # key = request.form.get("key")
-    # modelSelection = request.form.get("model")
-    # print(audio_url)
-
-    # if (modelSelection == None):
-    #     modelSelection = "small"
-    # model = whisper.load_model(modelSelection)
-    # print(model)
-
-    # # reject if key not the same 
-    # apiKey = st.secrets["Api-Key"]
-    # if apiKey != key:
-    #     return jsonify({
-    #         "image_url": image_url,
-    #         "model": model,
-    #         "result": "Invalid API Key",
-    #     }), 400
-
 
     response = requests.get(audio_url)
 
@@ -52,8 +32,6 @@ def runApi():
             "result": "Unable to save file, status code:  {response.status_code}" ,
         }), 400
 
-    # arr = np.asarray(bytearray(response.content), dtype=np.uint8)
-    # result = model.transcribe("audio.mp3")
     audio = "audio.mp3"
 
     audio = whisper.load_audio(audio)
@@ -78,7 +56,10 @@ def runApi():
     })
 
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+    options = {
+        'gateway_timeout': 600
+    }
+    app.run(host="0.0.0.0", port=7860, options=options)
         
 # def inference(audio):
 #     audio = whisper.load_audio(audio)

requirements.txt

@@ -1,4 +1,3 @@
 transformers
 requests
-streamlit
 flask