Update app.py

app.py

@@ -13,64 +13,65 @@ from pixeltable.functions import openai
 if 'OPENAI_API_KEY' not in os.environ:
     os.environ['OPENAI_API_KEY'] = getpass.getpass('Enter your OpenAI API key:')
 
-# Create a Table, a View, and Computed Columns
-
-pxt.drop_dir('directory', force=True)
-pxt.create_dir('directory')
-
-t = pxt.create_table(
-    'directory.video_table', {
-    "video": pxt.VideoType(nullable=True),
-    "sm_type": pxt.StringType(nullable=True),
-    }
-)
-
-frames_view = pxt.create_view(
-    "directory.frames",
-    t,
-    iterator=FrameIterator.create(video=t.video, fps=1)
-)
-
-# Create computed columns to store transformations and persist outputs
-t['audio'] = extract_audio(t.video, format='mp3')
-t['metadata'] = get_metadata(t.audio)
-t['transcription'] = openai.transcriptions(audio=t.audio, model='whisper-1')
-t['transcription_text'] = t.transcription.text
-
-# Custom UDF for Generating Social Media Prompts
-
-#Custom User-Defined Function (UDF) for Generating Social Media Prompts
-@pxt.udf
-def prompt(A: str, B: str) -> list[dict]:
-    system_msg = 'You are an expert in creating social media content and you generate effective post, based on user content. Respect the social media platform guidelines and constraints.'
-    user_msg = f'A: "{A}" \n B: "{B}"'
-    return [
-        {'role': 'system', 'content': system_msg},
-        {'role': 'user', 'content': user_msg}
-    ]
-
-# Apply the UDF to create a new column
-t['message'] = prompt(t.sm_type, t.transcription_text)
-
-"""## Generating Responses with OpenAI's GPT Model"""
-
-# # Generate responses using OpenAI's chat completion API
-t['response'] = openai.chat_completions(messages=t.message, model='gpt-4o-mini-2024-07-18', max_tokens=500)
-
-## Extract the content of the response
-t['answer'] = t.response.choices[0].message.content
-
 MAX_VIDEO_SIZE_MB = 35
 CONCURRENCY_LIMIT = 1
 
 def process_and_generate_post(video_file, social_media_type, progress=gr.Progress()):
+    
+    progress(0, desc="Initializing...")
+    
+    # Create a Table, a View, and Computed Columns
+
+    pxt.drop_dir('directory', force=True)
+    pxt.create_dir('directory')
+    
+    t = pxt.create_table(
+        'directory.video_table', {
+        "video": pxt.VideoType(nullable=True),
+        "sm_type": pxt.StringType(nullable=True),
+        }
+    )
+    
+    frames_view = pxt.create_view(
+        "directory.frames",
+        t,
+        iterator=FrameIterator.create(video=t.video, fps=1)
+    )
+    
+    # Create computed columns to store transformations and persist outputs
+    t['audio'] = extract_audio(t.video, format='mp3')
+    t['metadata'] = get_metadata(t.audio)
+    t['transcription'] = openai.transcriptions(audio=t.audio, model='whisper-1')
+    t['transcription_text'] = t.transcription.text
+    
+    # Custom UDF for Generating Social Media Prompts
+    
+    #Custom User-Defined Function (UDF) for Generating Social Media Prompts
+    @pxt.udf
+    def prompt(A: str, B: str) -> list[dict]:
+        system_msg = 'You are an expert in creating social media content and you generate effective post, based on user content. Respect the social media platform guidelines and constraints.'
+        user_msg = f'A: "{A}" \n B: "{B}"'
+        return [
+            {'role': 'system', 'content': system_msg},
+            {'role': 'user', 'content': user_msg}
+        ]
+    
+    # Apply the UDF to create a new column
+    t['message'] = prompt(t.sm_type, t.transcription_text)
+    
+    """## Generating Responses with OpenAI's GPT Model"""
+    
+    # # Generate responses using OpenAI's chat completion API
+    t['response'] = openai.chat_completions(messages=t.message, model='gpt-4o-mini-2024-07-18', max_tokens=500)
+    
+    ## Extract the content of the response
+    t['answer'] = t.response.choices[0].message.content
+    
     if not video_file:
         return "Please upload a video file.", None
 
     try:
         
-        progress(0, desc="Initializing...")
-        
         # Check video file size
         video_size = os.path.getsize(video_file) / (1024 * 1024)  # Convert to MB
         if video_size > MAX_VIDEO_SIZE_MB: