reconfigure software ai stack

active/software_ai_stack/openai-example.py

@@ -0,0 +1,133 @@
+import base64
+import os
+from datetime import datetime
+from io import BytesIO
+
+import requests
+from PIL import Image
+
+# Configuration
+BASE_URL = "https://llama-cpp.reeselink.com"
+API_KEY = os.getenv("LLAMA_CPP_API_KEY", "")  # Set if required
+
+
+def call_api(endpoint, method="GET", data=None):
+    """Generic API call helper"""
+    url = f"{BASE_URL}/v1/{endpoint}"
+    headers = {"Content-Type": "application/json"}
+    if API_KEY:
+        headers["Authorization"] = f"Bearer {API_KEY}"
+
+    response = requests.request(method, url, headers=headers, json=data)
+    return response
+
+
+# 1. List Models
+models_response = call_api("models")
+models = models_response.json().get("data", [])
+print(f"Available models: {[m['id'] for m in models]}")
+
+# 2. Use First Model
+model_id = models[1]["id"]
+
+# 3. Chat Completion
+chat_data = {
+    "model": model_id,
+    "messages": [
+        {"role": "system", "content": "You are helpful."},
+        {"role": "user", "content": "Tell me about Everquest!"},
+    ],
+    "temperature": 0.95,
+    "max_tokens": 100,
+}
+response = call_api("chat/completions", "POST", chat_data)
+print(response.json()["choices"][0]["message"]["content"])
+
+
+def describe_image(image_path, api_key=None):
+    """
+    Send an image to the LLM for description
+    """
+    base_url = "https://llama-cpp.reeselink.com"
+
+    # Read and encode image to base64
+    with open(image_path, "rb") as f:
+        encoded_image = base64.b64encode(f.read()).decode("utf-8")
+
+    # Prepare headers
+    headers = {"Content-Type": "application/json"}
+    if api_key:
+        headers["Authorization"] = f"Bearer {api_key}"
+
+    # Create payload
+    payload = {
+        "model": "qwen3-vl-30b-a3b-instruct",  # 👁️ VISION MODEL
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Describe this image in detail"},
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": f"data:image/jpeg;base64,{encoded_image}"},
+                    },
+                ],
+            }
+        ],
+        "max_tokens": 1000,
+        "temperature": 0.7,
+    }
+
+    # Send request
+    response = requests.post(
+        f"{base_url}/v1/chat/completions", headers=headers, json=payload
+    )
+
+    if response.status_code == 200:
+        return response.json()["choices"][0]["message"]["content"]
+    else:
+        print(f"Error: {response.status_code}")
+        print(response.text)
+        return None
+
+
+# description = describe_image("generated-image.png", api_key="your_key")
+# print(description)
+
+
+def generate_image(prompt, **kwargs):
+    """
+    Generate image using Stable Diffusion / OpenAI compatible API
+    """
+    base_url = "http://toybox.reeselink.com:1234/v1"
+
+    payload = {"model": "default", "prompt": prompt, "n": 1, "size": "1024x1024"}
+
+    response = requests.post(
+        f"http://toybox.reeselink.com:1234/v1/images/generations",
+        json=payload,
+        timeout=120,
+    )
+
+    if response.status_code == 200:
+        result = response.json()
+        # Save image
+        image_data = base64.b64decode(result["data"][0]["b64_json"])
+        img = Image.open(BytesIO(image_data))
+        filename = f"generated_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png"
+        img.save(filename)
+        print(f"✅ Saved: {filename}")
+        return result
+    else:
+        print(f"❌ Error: {response.status_code}")
+        print(response.text)
+        return None
+
+
+# Usage:
+result = generate_image(
+    prompt="A beautiful sunset over mountains, photorealistic",
+    negative_prompt="blurry, low quality",
+    steps=8,
+    guidance=7.5,
+)