Update app.py

app.py

@@ -1,3 +1,4 @@
+
 import pixeltable as pxt
 import os
 import openai
@@ -8,14 +9,16 @@ from pixeltable.functions.video import extract_audio
 from pixeltable.functions.audio import get_metadata
 from pixeltable.functions import openai
 
+"""## Store OpenAI API Key"""
+
 if 'OPENAI_API_KEY' not in os.environ:
     os.environ['OPENAI_API_KEY'] = getpass.getpass('Enter your OpenAI API key:')
 
-# Create a Pixeltable directory to organize related tables
+"""## Create a Table, a View, and Computed Columns"""
+
 pxt.drop_dir('directory', force=True)
 pxt.create_dir('directory')
 
-# Create a table to store video data
 t = pxt.create_table(
     'directory.video_table', {
     "video": pxt.VideoType(nullable=True),
@@ -23,11 +26,10 @@ t = pxt.create_table(
     }
 )
 
-# Create a view that automatically extracts frames from videos
 frames_view = pxt.create_view(
     "directory.frames",
     t,
-    iterator=FrameIterator.create(video=t.video, num_frames=2)
+    iterator=FrameIterator.create(video=t.video, fps=.25)
 )
 
 # Create computed columns to store transformations and persist outputs
@@ -36,19 +38,27 @@ t['metadata'] = get_metadata(t.audio)
 t['transcription'] = openai.transcriptions(audio=t.audio, model='whisper-1')
 t['transcription_text'] = t.transcription.text
 
-# Create a user-defined function (UDF) to construct the prompt
-# This shows how Pixeltable allows users to extend functionality with custom Python code
+"""## 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': 'You are an expert in creating social media content and you generate effective post, based on the video transcript and the type of social media asked for. Please respect the limitations in terms of characters and size of each social media platform'},
-        {'role': 'user', 'content': f'A: "{A}" \n B: "{B}"'}
+        {'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)
 
-# Import a function from Pixeltable's built-in library for OpenAI
+"""## 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
@@ -77,7 +87,7 @@ def process_and_generate_post(video_file, social_media_type):
 
         # Retrieve thumbnails
         thumbnails = frames_view.select(frames_view.frame).tail(4)['frame']
-      
+
         # Retrieve Pixeltable Table containing all videos and stored data
         df_output = t.collect().to_pandas()
 
@@ -139,7 +149,7 @@ def gradio_interface():
                     height='400px'
                 )
                 audio = gr.Audio()
-                
+
         df_output = gr.DataFrame(label="Pixeltable Table")
 
         generate_btn.click(
@@ -160,4 +170,7 @@ def gradio_interface():
 
 # Launch the Gradio interface
 if __name__ == "__main__":
-    gradio_interface().launch(show_api=False)
+    gradio_interface().launch(show_api=False)
+
+"""
+This example showcases how Pixeltable simplifies complex video processing workflows and integrates AI capabilities to create a powerful tool for generating social media content from video inputs."""