Update app.py

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+import google.generativeai as genai
+from pathlib import Path
+import tempfile
+
+
+
+def summarize_video(video_path):
+    if video_path is None:
+        return "Please upload a video file."
+    
+    try:
+        # Create a temporary file to store the video
+        with tempfile.NamedTemporaryFile(suffix=Path(video_path.name).suffix, delete=False) as tmp_file:
+            tmp_file.write(video_path.read())
+            video_file_path = tmp_file.name
+
+        # Create the prompt
+        prompt = "Summarize this video"
+
+        # Set up the model
+        model = genai.GenerativeModel(model_name="models/gemini-1.5-pro", api_key=os.environ['GOOGLE_API_KEY'])
+
+        # Make the LLM request
+        print("Making LLM inference request...")
+        response = model.generate_content([prompt, video_file_path],
+                                        request_options={"timeout": 2000})
+        
+        return response.text
+
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+    
+    finally:
+        # Clean up temporary file
+        if 'video_file_path' in locals():
+            Path(video_file_path).unlink(missing_ok=True)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=summarize_video,
+    inputs=gr.Video(label="Upload Video"),
+    outputs=gr.Textbox(label="Summary", lines=10),
+    title="Video Summarizer",
+    description="Upload a video to get an AI-generated summary using Gemini 1.5 Pro.",
+    examples=[],
+    cache_examples=False
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch(share=True)