Create Step 3.py

FabricSamples/# Configuration AZURE_OPENAI_KEY = "<Your Azure OpenAI key>" AZURE_OPENAI_GPT4O_ENDPOINT = "<Your Azure OpenAI gpt-4o deployment endpoint>" IMAGE_PATH = "<Path to your uploaded image file>" # For example, "/lakehouse/default/files/images/Image to Pipeline with AI/Step 3.py

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+# Summarize the image
+
+## Request payload
+payload = {
+    "messages": [
+    {
+        "role": "system",
+        "content": [
+        {
+            "type": "text",
+            "text": "You are an AI assistant that helps an Azure engineer understand an image that likely shows a Data Factory in Microsoft Fabric data pipeline. Show list of pipeline activities and how they are connected."
+        }
+        ]
+    },
+    {
+        "role": "user",
+        "content": [
+        {
+            "type": "image_url",
+            "image_url": {
+            "url": f"data:image/jpeg;base64,{encoded_image}"
+            }
+        }
+        ]
+    }
+    ],
+    "temperature": 0.7,
+    "top_p": 0.95,
+    "max_tokens": 800
+}
+
+## Send request
+try:
+    response = requests.post(AZURE_OPENAI_GPT4O_ENDPOINT, headers=headers, json=payload)
+    response.raise_for_status()  # Will raise an HTTPError if the HTTP request returned an unsuccessful status code
+except requests.RequestException as e:
+    raise SystemExit(f"Failed to make the request. Error: {e}")
+
+response_json = response.json()
+
+## Show AI response
+print(response_json["choices"][0]['message']['content'])