Update app.py

app.py

@@ -67,7 +67,7 @@ def segment_text(segments: list[dict]) -> list[str]:
 
 def summarize_text(text: str) -> str:
     """
-    Summarize a chunk of transcript via GPT.
+    Summarize a chunk of transcript via GPT-4.
     """
     prompt = f"Summarize the following lecture segment in 2-3 sentences:\n\n{text}"
     response = openai.ChatCompletion.create(
@@ -136,23 +136,20 @@ def run_pipeline(video_file: str) -> list[dict]:
         })
     return timeline
 
-
-def process_video(api_key: str, video_path: str) -> list[dict]:
-    """
-    Gradio wrapper: sets API key and runs pipeline on uploaded video.
-    """
-    openai.api_key = api_key
-    return run_pipeline(video_path)
-
 # Gradio interface for Hugging Face Spaces
-with gr.Blocks() as demo:
-    gr.Markdown("# Lecture Capture AI Pipeline\nEnter your OpenAI API key and upload a lecture video to generate a summarized timeline.")
-    api_key_input = gr.Textbox(label="OpenAI API Key", type="password", placeholder="sk-...", lines=1)
+demo = gr.Blocks()
+with demo:
+    gr.Markdown("# Lecture Capture AI Pipeline\nUpload a lecture video to generate a summarized timeline.")
     video_input = gr.Video(label="Lecture Video File")
     run_button = gr.Button("Process Video")
     output = gr.JSON(label="Generated Timeline")
 
-    run_button.click(fn=process_video, inputs=[api_key_input, video_input], outputs=output)
+    run_button.click(fn=lambda video_path: run_pipeline(video_path), inputs=video_input, outputs=output)
 
 if __name__ == "__main__":
-    demo.launch()
+    # Prompt for API key if not set in environment
+    api_key = os.getenv("OPENAI_API_KEY")
+    if not api_key:
+        api_key = input("Enter your OpenAI API key: ")
+    openai.api_key = api_key
+    demo.launch()