Update app.py

app.py

@@ -13,8 +13,8 @@ import openai
 # Load environment variables
 load_dotenv()
 
-# Initialize the OpenAI client
-client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+# Set up OpenAI API
+openai.api_key = os.getenv("OPENAI_API_KEY")
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
@@ -30,18 +30,17 @@ def transcribe_audio(file):
     logging.info("Transcribing audio file")
     file = convert_to_supported_format(file)
     logging.info("Converted file to WAV format")
-
+    
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
         temp_file.write(file.getvalue())
         temp_file_path = temp_file.name
 
     try:
-        with open(temp_file_path, "rb") as audio_file:
-            transcript = client.audio.transcriptions.create(
-                model="whisper-1", 
-                file=audio_file
-            )
-        return transcript.text
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            with open(temp_file_path, "rb") as audio_file:
+                transcript = openai.Audio.transcribe("whisper-1", audio_file)
+        return transcript["text"]
     except Exception as e:
         logging.error(f"Error in transcription: {str(e)}")
         return f"Error in transcription: {str(e)}"
@@ -63,7 +62,7 @@ def get_transcript(url):
 
 def summarize_text(text):
     try:
-        response = client.chat.completions.create(
+        response = openai.ChatCompletion.create(
             model="gpt-3.5-turbo",
             messages=[
                 {"role": "system", "content": "You are a helpful assistant."},
@@ -71,7 +70,7 @@ def summarize_text(text):
             ],
             max_tokens=150
         )
-        return response.choices[0].message.content.strip()
+        return response['choices'][0]['message']['content'].strip()
     except Exception as e:
         return f"Error in summarizing text: {str(e)}"
 
@@ -100,3 +99,5 @@ elif option == "Upload audio/video file":
                 transcript_text = transcribe_audio(uploaded_file)
             summary = handle_summary(transcript_text)
             st.text_area("Summary", summary, height=200)
+if __name__ == "__main__":
+    main()