Update app.py

app.py

@@ -74,53 +74,109 @@
 # iface = gr.Interface(fn=function1, inputs="text", outputs=[gr.Audio(label="Audio",type="numpy")])
 # iface.launch()
 
+# import gradio as gr
+# import requests
+# import urllib.request
+# from pydub import AudioSegment
+# import numpy as np
+# import os
+# import sys
+# import wave
+# import io
+# import base64
+# import azure.cognitiveservices.speech as speechsdk
+
+# speech_key = os.environ.get("test3")
+# service_region = os.environ.get("test4")
+
+# speech_config = speechsdk.SpeechConfig(subscription=speech_key, region=service_region)
+# # Note: the voice setting will not overwrite the voice element in input SSML.
+# speech_config.speech_synthesis_voice_name = os.environ.get("test5")
+
+# def function1(prompt):
+#     response = requests.post("https://tommy24-testing3.hf.space/run/predict", json={
+#       "data": [
+#         prompt,
+#     ]}).json()
+#     message = response["data"][0]
+#     speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config)
+#     result = speech_synthesizer.speak_text_async(message).get()
+#     if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:
+#         audio_stream = io.BytesIO(result.audio_data)
+    
+#         # Create a wave file object and write the audio data to it
+#         with wave.open("audio.wav", 'wb') as wave_file:
+#             wave_file.setnchannels(1)
+#             wave_file.setsampwidth(2)
+#             wave_file.setframerate(16000)
+#             wave_file.writeframesraw(audio_stream.getvalue())
+        
+#         # Use ffmpeg to convert the wave file to an mp3 file
+#         filename = "output.mp3"
+        
+#         if os.path.exists(filename):
+#             os.remove(filename)
+#         else:
+#             pass
+#         command = f"ffmpeg -i audio.wav -y -codec:a libmp3lame -qscale:a 2 {filename}"
+#         os.system(command)
+#         return "output.mp3"
+
+# iface = gr.Interface(fn=function1, inputs="text", outputs=[gr.Audio(label="Audio",type="numpy")])
+# iface.launch()
+
 import gradio as gr
 import requests
-import urllib.request
-from pydub import AudioSegment
-import numpy as np
 import os
-import sys
-import wave
-import io
-import base64
-import azure.cognitiveservices.speech as speechsdk
-
-speech_key = os.environ.get("test3")
-service_region = os.environ.get("test4")
-
-speech_config = speechsdk.SpeechConfig(subscription=speech_key, region=service_region)
-# Note: the voice setting will not overwrite the voice element in input SSML.
-speech_config.speech_synthesis_voice_name = os.environ.get("test5")
 
-def function1(prompt):
+def function2(prompt):
     response = requests.post("https://tommy24-testing3.hf.space/run/predict", json={
       "data": [
         prompt,
     ]}).json()
     message = response["data"][0]
-    speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config)
-    result = speech_synthesizer.speak_text_async(message).get()
-    if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted:
-        audio_stream = io.BytesIO(result.audio_data)
-    
-        # Create a wave file object and write the audio data to it
-        with wave.open("audio.wav", 'wb') as wave_file:
-            wave_file.setnchannels(1)
-            wave_file.setsampwidth(2)
-            wave_file.setframerate(16000)
-            wave_file.writeframesraw(audio_stream.getvalue())
-        
-        # Use ffmpeg to convert the wave file to an mp3 file
-        filename = "output.mp3"
-        
-        if os.path.exists(filename):
-            os.remove(filename)
-        else:
-            pass
-        command = f"ffmpeg -i audio.wav -y -codec:a libmp3lame -qscale:a 2 {filename}"
-        os.system(command)
-        return "output.mp3"
-
-iface = gr.Interface(fn=function1, inputs="text", outputs=[gr.Audio(label="Audio",type="numpy")])
+
+    url = "https://api.dynapictures.com/designs/7c4aba1d73"
+    headers = {
+        "Authorization": "Bearer d11937f0c9b7f55db1916f11abbe05c5917798f562506f11",
+        "Content-Type": "application/json"
+    }
+
+    payload = {
+      "format": "jpeg",
+      "metadata": "some text",
+      "params": [
+        {
+          "name": "bubble",
+          "imageUrl": "https://dynapictures.com/b/rest/public/media/0ceb636a01/images/568b337221.png"
+        },
+        {
+          "name": "quotes",
+          "imageUrl": "https://dynapictures.com/b/rest/public/media/0ceb636a01/images/779f8b9041.png"
+        },
+        {
+          "name": "text",
+          "text": message
+        },
+        {
+          "name": "avatar",
+          "imageUrl": "https://dynapictures.com/b/rest/public/media/0ceb636a01/images/2f7ddd7b55.jpg"
+        },
+        {
+          "name": "name",
+          "text": "JohnAI"
+        },
+        {
+          "name": "title",
+          "text": "Automated"
+        }
+      ]
+    }
+
+    response = requests.post(url, headers=headers, data=json.dumps(payload))
+    response = response.json()
+    response = response["imageUrl"]
+    return response
+
+iface = gr.Interface(fn=function1, inputs="text", outputs="text")
 iface.launch()