combined pipeline running function

3d-generation-pipeline/notebooks/cloudflare_API_test.ipynb

@@ -76,18 +76,6 @@
     "with open(out_path, \"wb\") as f:\n",
     "    f.write(img_bytes)\n",
     "\n",
-    "print(f\"Saved: {out_path}  ({len(img_bytes)} bytes)\")\n",
-    "\n",
-    "b64 = data[\"result\"][\"image\"]\n",
-    "if not b64:\n",
-    "    raise RuntimeError(f\"Unexpected response structure: {data}\")\n",
-    "\n",
-    "img_bytes = base64.b64decode(b64)\n",
-    "\n",
-    "out_path = \"image9.jpg\"\n",
-    "with open(out_path, \"wb\") as f:\n",
-    "    f.write(img_bytes)\n",
-    "\n",
     "print(f\"Saved: {out_path}  ({len(img_bytes)} bytes)\")"
    ]
   },
@@ -138,14 +126,6 @@
     "result_text = data[\"result\"][\"response\"]\n",
     "print(result_text)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "76fa21f0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

3d-generation-pipeline/notebooks/subprocess_test.ipynb

@@ -0,0 +1,82 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4826c91d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2025-10-18-16-35-47'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9419e692",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import subprocess\n",
+    "\n",
+    "# Path to the Python interpreter in the other virtual environment\n",
+    "venv_python = r\"/path/to/other/venv/bin/python\"  # On Windows: r\"C:\\path\\to\\other\\venv\\Scripts\\python.exe\"\n",
+    "\n",
+    "# Path to the .py file you want to run\n",
+    "script_path = r\"/path/to/your_script.py\"\n",
+    "\n",
+    "# Optional: arguments to pass to the script\n",
+    "args = [\"arg1\", \"arg2\"]\n",
+    "\n",
+    "# Build the command\n",
+    "command = [venv_python, script_path] + args\n",
+    "\n",
+    "try:\n",
+    "    # Run the subprocess\n",
+    "    result = subprocess.run(command, capture_output=True, text=True)\n",
+    "\n",
+    "    # Print output and errors\n",
+    "    print(\"STDOUT:\\n\", result.stdout)\n",
+    "    print(\"STDERR:\\n\", result.stderr)\n",
+    "    print(\"Return Code:\", result.returncode)\n",
+    "\n",
+    "except Exception as e:\n",
+    "    print(f\"Error occurred: {e}\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

3d-generation-pipeline/start_pipeline.py

@@ -0,0 +1,75 @@
+import os
+import base64
+import requests
+from datetime import datetime
+from dotenv import load_dotenv
+
+load_dotenv()
+
+ACCOUNT_ID = os.environ["CF_ACCOUNT_ID"]
+API_TOKEN = os.environ["CF_API_TOKEN"]
+
+
+def get_timestamp():
+    return datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
+
+
+def text_to_image(prompt, output_name):
+    MODEL = "@cf/black-forest-labs/flux-1-schnell"
+    URL = f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}"
+
+    payload = {
+        "prompt": prompt,
+    }
+
+    headers = {
+        "Authorization": f"Bearer {API_TOKEN}",
+        "Content-Type": "application/json",
+    }
+
+    resp = requests.post(URL, json=payload, headers=headers, timeout=60)
+    resp.raise_for_status()
+
+    data = resp.json()
+    b64 = data["result"]["image"]
+    if not b64:
+        raise RuntimeError(f"Unexpected response structure: {data}")
+
+    img_bytes = base64.b64decode(b64)
+
+    out_path = f"images/{output_name}.jpg"
+    with open(out_path, "wb") as f:
+        f.write(img_bytes)
+
+    return out_path
+
+def refine_text_prompt(prompt):
+    MODEL = "@cf/meta/llama-3.2-3b-instruct"
+    URL = f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}"
+
+    instructions = """
+    User is talking about some object. Your task is to generate a short and concise description of it. Use only user's own words, keep it as short as possible.
+    Example:
+    User: 'Umm, okay, I would like a really cool sword, with for example a bright orange crossguard. And also it should be slightly curved.'
+    You: 'a slightly curved sword with bright orange crossguard'
+    """
+
+    response = requests.post(URL,
+    headers={"Authorization": f"Bearer {API_TOKEN}"},
+    json={
+        "messages": [
+        {"role": "system", "content": instructions},
+        {"role": "user", "content": prompt}
+        ]
+    }
+    )
+    data = response.json()
+    return data["result"]["response"]
+
+def main():
+    user_prompt = "Give epic sword"
+    print(f"User prompt: {user_prompt}")
+    refined_prompt = refine_text_prompt(user_prompt)
+    print(f"Refined prompt: {refined_prompt}")
+    image_path = text_to_image(refined_prompt, get_timestamp())
+    print(f"Generated image file: {image_path}")