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-4.
+    Summarize a chunk of transcript via GPT.
     """
     prompt = f"Summarize the following lecture segment in 2-3 sentences:\n\n{text}"
     response = openai.ChatCompletion.create(
@@ -100,10 +100,11 @@ def extract_frame(video_path: str, timestamp: str, output_path: str) -> None:
     subprocess.run(cmd, check=True)
 
 
-def run_pipeline(video_file: str) -> list[dict]:
+def run_pipeline(api_key: str, video_file: str) -> list[dict]:
     """
     Execute the full pipeline and return timeline as a list of dicts.
     """
+    openai.api_key = api_key
     chunks = chunk_video(video_file)
     all_segments = []
     for chunk in chunks:
@@ -139,17 +140,13 @@ def run_pipeline(video_file: str) -> list[dict]:
 # Gradio interface for Hugging Face Spaces
 demo = gr.Blocks()
 with demo:
-    gr.Markdown("# Lecture Capture AI Pipeline\nUpload a lecture video to generate a summarized timeline.")
+    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)
     video_input = gr.Video(label="Lecture Video File")
     run_button = gr.Button("Process Video")
     output = gr.JSON(label="Generated Timeline")
 
-    run_button.click(fn=lambda video_path: run_pipeline(video_path), inputs=video_input, outputs=output)
+    run_button.click(fn=run_pipeline, inputs=[api_key_input, video_input], outputs=output)
 
 if __name__ == "__main__":
-    # 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()