update for 3.1 native tool use support

tutorials/llamaguard/ai_guard.py

@@ -0,0 +1,96 @@
+import gradio as gr
+import base64
+from PIL import Image
+import io
+import requests
+from groq import Groq
+
+# Groq API settings
+LLAVA_MODEL = "llava-v1.5-7b-4096-preview"
+LLAMA_GUARD_MODEL = "llama-guard-3-8b"
+
+def encode_image(image):
+    buffered = io.BytesIO()
+    image.save(buffered, format="JPEG")
+    return base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+def analyze_image(image, prompt, api_key, is_url=False):
+    client = Groq(api_key=api_key)
+
+    if is_url:
+        image_content = {"type": "image_url", "image_url": {"url": image}}
+    else:
+        base64_image = encode_image(image)
+        image_content = {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
+
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": prompt},
+                        image_content,
+                    ],
+                }
+            ],
+            model=LLAVA_MODEL,
+        )
+        return chat_completion.choices[0].message.content
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def check_content_safety(image_description, api_key):
+    client = Groq(api_key=api_key)
+
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {"role": "system", "content": "You are a content safety classifier. Analyze the given text and determine if it contains any unsafe or inappropriate content."},
+                {"role": "user", "content": f"Please analyze this image description for any unsafe or inappropriate content: {image_description}"}
+            ],
+            model=LLAMA_GUARD_MODEL,
+        )
+        return chat_completion.choices[0].message.content
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def process_image(image, url, prompt, api_key):
+    if image is not None:
+        return analyze_image(image, prompt, api_key), check_content_safety(analyze_image(image, prompt, api_key), api_key)
+    elif url:
+        try:
+            response = requests.get(url)
+            image = Image.open(io.BytesIO(response.content))
+            return analyze_image(url, prompt, api_key, is_url=True), check_content_safety(analyze_image(url, prompt, api_key, is_url=True), api_key)
+        except:
+            return "Invalid image URL. Please provide a direct link to an image.", ""
+    else:
+        return "Please provide an image to analyze.", ""
+
+def launch():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Image Analysis with Groq API")
+
+        with gr.Row():
+            with gr.Column():
+                api_key = gr.Textbox(label="Enter your Groq API Key:", type="password")
+                image_input = gr.Image(type="pil", label="Upload Image")
+                url_input = gr.Textbox(label="Or paste image URL")
+                prompt = gr.Textbox(label="Enter a prompt for image analysis:", value="Describe the contents of this image in detail.")
+                analyze_button = gr.Button("Analyze Image")
+
+            with gr.Column():
+                analysis_output = gr.Textbox(label="Analysis Result")
+                safety_output = gr.Textbox(label="Content Safety Check")
+
+        analyze_button.click(
+            fn=process_image,
+            inputs=[image_input, url_input, prompt, api_key],
+            outputs=[analysis_output, safety_output]
+        )
+
+    demo.launch()
+
+if __name__ == "__main__":
+    launch()

tutorials/llamaguard/image-analysis-app/.next/server/app-paths-manifest.json

@@ -0,0 +1 @@
+{}

tutorials/llamaguard/image-analysis-app/.next/server/interception-route-rewrite-manifest.js

@@ -0,0 +1 @@
+self.__INTERCEPTION_ROUTE_REWRITE_MANIFEST="[]"

tutorials/llamaguard/image-analysis-app/.next/server/middleware-manifest.json

@@ -0,0 +1,6 @@
+{
+  "version": 3,
+  "middleware": {},
+  "functions": {},
+  "sortedMiddleware": []
+}

tutorials/llamaguard/image-analysis-app/.next/server/pages-manifest.json

@@ -0,0 +1 @@
+{}

tutorials/llamaguard/image-analysis-app/.next/server/server-reference-manifest.json

@@ -0,0 +1,5 @@
+{
+  "node": {},
+  "edge": {},
+  "encryptionKey": "RsP/1g1afXOf67tygDO4r89ef0cmb0oL2lgcE0BofgA="
+}

tutorials/llamaguard/image-analysis-app/.next/types/package.json

@@ -0,0 +1 @@
+{"type": "module"}

tutorials/parallel-tool-use/parallel-tool-use.ipynb

@@ -29,12 +29,20 @@
    "id": "67e90d90",
    "metadata": {},
    "source": [
-    "## Parallel Tool Use with Groq API 💪\n",
-    "Tool use support is available for all text-to-text models and parallel tool use support is enabled for all Llama 3 and Llama 3.1 models. For a better tool use experience, Groq also released two open-source models fine-tuned for tool use in collaboration with [Glaive AI](https://glaive.ai/):\n",
+    "## Parallel Tool Use and Function Calling with Groq API 💪\n",
+    "Tool use, or function calling, support is available for all text models and parallel tool use support is enabled for all Llama 3 and Llama 3.1 models. The Llama 3.1 models now support the native tool use format that was used in post-training, which results in much better quality, especially in multi-turn conversations and parallel tool calling.\n",
+    "\n",
+    "Groq also released two open-source models fine-tuned for tool use in collaboration with [Glaive AI](https://glaive.ai/), which are available on HuggingFace and via Groq API for your applications:\n",
     "- [groq/Llama-3-Groq-70B-Tool-Use](https://huggingface.co/Groq/Llama-3-Groq-70B-Tool-Use)\n",
     "- [groq/Llama-3-Groq-8B-Tool-Use](https://huggingface.co/Groq/Llama-3-Groq-8B-Tool-Use)\n",
     "\n",
-    "These models are available on HuggingFace and via Groq API for your applications! Read more about the training approach and benchmarking results [here](https://groq.com/introducing-llama-3-groq-tool-use-models/). \n",
+    "Read more about the training approach and benchmarking results [here](https://groq.com/introducing-llama-3-groq-tool-use-models/). \n",
+    "\n",
+    "For your applications that require tool use, we highly recommend trying the following models:\n",
+    "- `llama3-groq-70b-8192-tool-use-preview`\n",
+    "- `llama3-groq-8b-8192-tool-use-preview`\n",
+    "- `llama-3.1-70b-versatile`\n",
+    "- `llama-3.1-8b-instant`\n",
     "\n",
     "In this basic tutorial, we will be reviewing the basic structure of parallel tool use with Groq API to fetch given product pricing for multiple bakery items.\n"
    ]