[Samples]: Add VLM alerts sample in python 1/2

dependencies/gstreamer.cmake

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     	                -Ddevtools=disabled
     	                -Dorc=disabled
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+						-Dpython=enabled
     	                -Dgst-plugins-base:nls=disabled
     	                -Dgst-plugins-base:gl=disabled
     	                -Dgst-plugins-base:xvideo=enabled

samples/gstreamer/python/vlm_alerts/README.md

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+# VLM Alerts
+
+This sample demonstrates an edge AI alerting pipeline using Vision-Language Models (VLMs).
+
+It shows how to:
+
+- Download a VLM from Hugging Face
+- Convert it to OpenVINO IR using `optimum-cli`
+- Run inference inside a DL Streamer pipeline
+- Generate structured JSON alerts per processed frame
+- Produce MP4 output
+
+## Use Case: Alert-Based Monitoring
+
+VLMs can help accurately detect rare or contextual events using natural language prompts — for example, detecting a police car in a traffic video.
+This enables alerting for events, like in prompts:
+
+- Is there a police car?
+- Is there smoke or fire?
+- Is a person lying on the ground?
+
+## Model Preparation
+
+Any image-text-to-text model supported by optimum-intel can be used. Smaller models (1B-4B parameters) are recommended for edge deployment. For example, OpenGVLab/InternVL3_5-2B.
+
+The script runs:
+
+```code
+optimum-cli export openvino \
+    --model <model_id> \
+    --task image-text-to-text \
+    --trust-remote-code \
+    <output_dir>
+```
+
+Exported artifacts are stored under `models/<ModelName>/`. 
+The export runs once and is cached. To skip export, pass `--model-path` directly.
+
+## Video Preparation
+
+Similarly to model, provide either:
+
+- `--video-path` for a local file
+- `--video-url` to download automatically
+
+Downloaded videos are cached under `videos/`. 
+
+## Pipeline Architecture
+
+The pipeline is built dynamically in Python using `Gst.parse_launch`.
+
+```mermaid
+graph LR
+    A[filesrc] --> B[decodebin3]
+    B --> C[gvagenai]
+    C --> D[gvametapublish]
+    D --> E[gvafpscounter]
+    E --> F[gvawatermark]
+    F --> G["encode (vah264enc + h264parse + mp4mux)"]
+    G --> H[filesink]
+```
+
+## Setup
+
+1. Create and activate a virtual environment:
+```code
+cd samples/gstreamer/python/vlm_alerts
+python3 -m venv .vlm-venv
+source .vlm-venv/bin/activate
+```
+
+2. Install dependencies:
+```code
+curl -LO https://raw.githubusercontent.com/openvinotoolkit/openvino.genai/refs/heads/releases/2026/0/samples/export-requirements.txt
+pip install -r export-requirements.txt PyGObject==3.50.0
+```
+
+> A DL Streamer build that includes the `gvagenai` element is required.
+
+## Running
+
+Required arguments:
+
+- `--prompt`
+- `--video-path` or `--video-url`
+- `--model-id` or `--model-path`
+
+Example:
+
+```code
+python3 vlm_alerts.py \
+    --video-url https://videos.pexels.com/video-files/2103099/2103099-hd_1280_720_60fps.mp4 \
+    --model-id OpenGVLab/InternVL3_5-2B \
+    --prompt "Is there a police car? Answer yes or no."
+```
+
+Optional arguments:
+
+| Argument | Default | Description |
+|---|---|---|
+| `--device` | `GPU` | Inference device |
+| `--max-tokens` | `20` | Maximum tokens in the model response |
+| `--frame-rate` | `1.0` | Frames per second passed to `gvagenai` |
+| `--videos-dir` | `./videos` | Directory for downloaded videos |
+| `--models-dir` | `./models` | Directory for exported models |
+| `--results-dir` | `./results` | Directory for output files |
+
+## Output
+
+```
+results/<ModelName>-<video_stem>.jsonl
+results/<ModelName>-<video_stem>.mp4
+```
+
+The `.jsonl` file contains one model response per processed frame and can be used to trigger downstream alerting logic.
+
+### Help
+
+To display all available arguments and defaults:
+
+```code
+python3 vlm_alerts.py --help