feat 新增 process lookbooks 接口

fix

app/service/lookbooks/service.py

@@ -0,0 +1,128 @@
+import json
+import os
+
+from openai import OpenAI
+
+from app.service.lookbooks.utils.image_utils import base64_encode_image, generate_text_id
+from app.service.lookbooks.utils.openai_utils import wait_for_job_completion
+
+OPENAI_API_KEY = "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"
+OPENAI_API_BASE = "https://pangkaichen-openai-prox-98.deno.dev/v1"
+client = OpenAI(
+    api_key=OPENAI_API_KEY,
+    base_url=OPENAI_API_BASE,
+)
+
+
+def create_image_batch_requests(
+        image_list,
+        output_path,
+        prompt="""You are an AI assistant specializing in fashion analysis and tagging for an advanced clothing indexing system. Your task is to analyze images of outfits and provide concise, relevant information. Please structure your response as follows:
+
+        1. Brief Summary: Start with a one-sentence summary of the overall style and vibe of the outfit.
+
+        2. Tags: Provide relevant tags in the following categories. Use multiple tags where appropriate, separated by commas.
+
+           Season: [e.g., Spring/Summer, Fall/Winter]
+           Style: [e.g., Minimalist, Bohemian, Streetwear, Business Casual]
+           Occasion: [e.g., Office, Casual, Party, Outdoor]
+           Colors: [List main colors used]
+           Materials: [e.g., Cotton, Denim, Leather, Silk]
+           Key Elements: [Any distinctive fashion elements]
+
+        3. Item Descriptions: 
+           - Briefly describe each main clothing item (top, bottom, outerwear). Number and categorize each item (e.g., 1. Top: ..., 2. Bottom: ...). Keep descriptions concise but include key details about style, color, and distinctive features.
+           - In a single sentence, list all accessories and their overall effect on the outfit.
+
+        Ensure your tags and descriptions are accurate, relevant to current fashion trends, and useful for indexing and retrieval purposes."""
+):
+    # 预处理 prompt：移除多余的空白和换行符
+    prompt = ' '.join(prompt.split())
+
+    completed_id = []
+    if os.path.exists(os.path.join(output_path, "image_description_results.jsonl")):
+        with open(os.path.join(output_path, "image_description_results.jsonl"), "r") as f:
+            for line in f:
+                completed_id.append(json.loads(line)["custom_id"])
+
+    tasks = []
+    id2img = {}
+    for idx, image_filename in enumerate(image_list):
+        image_base64 = base64_encode_image(image_filename)
+        if not image_base64:
+            continue
+        current_id = generate_text_id(image_base64)
+        if current_id in completed_id:
+            continue
+        task = {
+            "custom_id": current_id,
+            "method": "POST",
+            "url": "/v1/chat/completions",
+            "body": {
+                "model": "gpt-4o",
+                "temperature": 1.0,
+                "max_tokens": 500,
+                "messages": [
+                    {
+                        "role": "system",
+                        "content": prompt
+                    },
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": f"data:image/jpeg;base64,{image_base64}",
+                                    "detail": "low"
+                                }
+                            }
+                        ]
+                    }
+                ]
+            }
+        }
+        tasks.append(task)
+        id2img[current_id] = image_filename
+    print(f"In total {len(tasks)} images")
+    if tasks:
+        batch_file_name = os.path.join(output_path, "image_batch_requests.jsonl")
+        with open(batch_file_name, 'w', encoding='utf-8') as file:
+            for obj in tasks:
+                file.write(json.dumps(obj, ensure_ascii=False) + '\n')
+
+        batch_file = client.files.create(
+            file=open(batch_file_name, "rb"),
+            purpose="batch"
+        )
+        batch_job = client.batches.create(
+            input_file_id=batch_file.id,
+            endpoint="/v1/chat/completions",
+            completion_window="24h"
+        )
+
+        if wait_for_job_completion(client, batch_job.id):
+            output_file_id = client.batches.retrieve(batch_job.id).output_file_id
+            file_response = client.files.content(output_file_id)
+            file_content_str = file_response.read().decode('utf-8')
+
+            with open(os.path.join(output_path, "image_description_results.jsonl"), "w", encoding='utf-8') as f:
+                for line in file_content_str.splitlines():
+                    if line.strip():
+                        try:
+                            result = json.loads(line)
+                            image_id = result['custom_id']
+                            caption = result['response']['body']['choices'][0]['message']['content']
+                            output = json.dumps({
+                                "custom_id": image_id,
+                                "summary": caption,
+                                "url": id2img[image_id]
+                            })
+                            f.write(output + '\n')
+                        except json.JSONDecodeError as error:
+                            print(f"Error parsing: {error} -- at line: {line}")
+        else:
+            print("Job failed")
+        return os.path.join(output_path, "image_description_results.jsonl")
+    else:
+        return None