New Nodes

Batch OpenAI Image Chat

classes/DATASET_OpenAIChatImageBatch.py

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+import base64
+import io
+from PIL import Image
+import numpy as np
+from openai import OpenAI
+import os
+
+class DATASET_OpenAIChatImageBatch:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "image_detail": (["low","high"], {"default": "high"}),
+                "prompt": ("STRING", {"multiline": True, "default": ""}),
+                "model": (["gpt-4o","gpt-4", "gpt-4-32k", "gpt-3.5-turbo", "gpt-4-0125-preview", "gpt-4-turbo-preview", "gpt-4-1106-preview", "gpt-4-0613"], {"default": "gpt-4o"}),
+                "api_url": ("STRING", {"multiline": False, "default": "https://api.openai.com/v1"}),
+                "api_key": ("STRING", {"multiline": False}),
+                "token_length": ("INT", {"default": 1024})
+            }
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("STRING",)
+    OUTPUT_IS_LIST = (True,)
+    FUNCTION = "generate"
+    CATEGORY = "🔶DATASET🔶"
+
+    def to_base64(self, image):
+        image = image[0]
+        i = 255. * image.cpu().numpy()
+        img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+        buffered = io.BytesIO()
+        img.save(buffered, format="PNG")
+        return base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+    def generate(self, images, image_detail, model, api_url, api_key, prompt, token_length):
+
+        try:
+
+            image_detail = image_detail[0]
+            model = model[0]
+            api_url = api_url[0]
+            api_key = api_key[0]
+            prompt = prompt[0]
+            token_length = token_length[0]
+
+            answers = []
+
+            for image in images:
+
+                ai = OpenAI(api_key=api_key, base_url=api_url)
+                base64img = self.to_base64(image)
+                if not api_key:
+                    return "OpenAI API key is required."
+
+                request = [{"role": "system","content": "You are GPT-4."}]
+                request.append({"role": "user","content": [{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64img}", "detail": image_detail}}]})
+                request.append({"role": "user","content": prompt})
+                response = ai.chat.completions.create(model=model,messages=request,max_tokens=token_length)
+                answer = response.choices[0].message.content
+
+                answers.append(answer)
+
+            return (answers,)
+
+        except Exception as e:
+            return (f"Error: {str(e)}",)
+    
+    @classmethod
+    def IS_CHANGED(s, image, image_detail, model, api_url, api_key, prompt, token_length):       
+       return os.urandom(16).hex()
+
+N_CLASS_MAPPINGS = {
+    "DATASET_OpenAIChatImageBatch": DATASET_OpenAIChatImageBatch,
+}
+
+N_DISPLAY_NAME_MAPPINGS = {
+    "DATASET_OpenAIChatImageBatch": "DATASET_OpenAIChatImageBatch",
+}

classes/DATASET_TXTFileSaverBatch.py

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+import os
+
+def save_file(filename, output_dir, content, mode='SaveNew'):
+    os.makedirs(output_dir, exist_ok=True)
+    file_path = os.path.join(output_dir, filename)
+    
+    if mode == 'SaveNew':
+        counter = 0
+        while os.path.exists(file_path):
+            counter += 1
+            file_path = os.path.join(output_dir, f"{os.path.splitext(filename)[0]}_{counter}{os.path.splitext(filename)[1]}")
+    elif mode == 'Merge' and os.path.exists(file_path):
+        with open(file_path, 'a') as file:
+            file.write(content)
+        print(f"Content appended successfully to {file_path}")
+        return
+    elif mode == 'Overwrite' and os.path.exists(file_path):
+        os.remove(file_path)
+    elif mode == 'MergeAndSaveNew' and os.path.exists(file_path):
+        with open(file_path, 'r') as file:
+            existing_content = file.read()
+        content = existing_content + content
+        counter = 0
+        while os.path.exists(file_path):
+            counter += 1
+            file_path = os.path.join(output_dir, f"{os.path.splitext(filename)[0]}_{counter}{os.path.splitext(filename)[1]}")
+    
+    with open(file_path, 'w') as file:
+        file.write(content)
+    print(f"File saved successfully at {file_path}")
+
+class DATASET_TXTFileSaverBatch:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "file_names": ("STRING",{"forceInput": True}),
+                "contents": ("STRING",{"forceInput": True}),
+                "save_in": ("STRING", {"default": "directory path"}),
+                "save_mode": (['Merge','Overwrite','SaveNew','MergeAndSaveNew'],),
+            },
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ()
+    FUNCTION = "SaveIT"
+    OUTPUT_NODE = True
+
+    CATEGORY = "🔶DATASET🔶"
+
+    def SaveIT(self, file_names, contents, save_in, save_mode):
+        try:
+
+            directory = save_in[0]
+            mode = save_mode[0]
+
+            if not os.path.exists(directory):
+                os.makedirs(directory)
+
+            for i in range(0, len(contents)):
+                text = contents[i]
+                file_name = file_names[i]
+                save_file(f"{file_name}.txt", directory, text, mode)
+
+        except Exception as e:
+            print(f"Error saving: {e}")
+
+        return ()
+    
+    @classmethod
+    def IS_CHANGED(s, content, file_name, directory, mode):       
+       return os.urandom(16).hex()
+
+
+N_CLASS_MAPPINGS = {
+    "DATASET_TXTFileSaverBatch": DATASET_TXTFileSaverBatch,
+}
+
+N_DISPLAY_NAME_MAPPINGS = {
+    "DATASET_TXTFileSaverBatch": "DATASET_TXTFileSaverBatch",
+}