Upload folder using huggingface_hub

README.md

@@ -1,12 +1,27 @@
 ---
-title: Suanfamama Cognitive Computational Advertising
-emoji: 🦀
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.40.0
+title: Suanfamama_Cognitive_Computational_Advertising
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 4.29.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 基于 gradio & Huggingface的 认知计算广告创作 算法
+## 核心算法创作步骤
+1. 步骤一：认知
+* 对广告主要有深刻认知，如需求，预算及折扣等
+2. 步骤二：计算 - 编写脚本和分镜
+![](./img/cca.step1.认知计算广告.png)
+3. 步骤三：计算 - 生成关键帧、修图、确认关键帧
+![](./img/cca.step2.认知计算广告.png)
+4. 步骤四：计算 - AI拍摄
+![](./img/cca.step3.认知计算广告.png)
+5. 步骤五：计算 - 配音、剪切及后期
+![](./img/cca.step4.认知计算广告.png)
+6. 步骤六：广告
+* 人工审阅及智能投放
+
+## 技术路线
+* 使用 python gradio lib 进行AI应用构建
+
+## How to Deploy
+* gradio deploy

app.py

@@ -0,0 +1,103 @@
+import gradio as gr
+from io import BytesIO
+import IPython
+import json
+import os
+from PIL import Image
+import requests
+import time
+import getpass
+
+# Get the API key
+STABILITY_API_KEY = os.environ.get('STABILITY_API_KEY')  # Try to get from environment variable
+if not STABILITY_API_KEY:
+    STABILITY_API_KEY = getpass.getpass('Enter your API Key: ')  # Prompt if not found
+
+def send_generation_request(
+    host,
+    params,
+):
+    headers = {
+        "Accept": "image/*",
+        "Authorization": f"Bearer {STABILITY_API_KEY}"
+    }
+
+    # Encode parameters
+    files = {}
+    image = params.pop("image", None)
+    mask = params.pop("mask", None)
+    if image is not None and image != '':
+        files["image"] = open(image, 'rb')
+    if mask is not None and mask != '':
+        files["mask"] = open(mask, 'rb')
+    if len(files)==0:
+        files["none"] = ''
+
+    # Send request
+    print(f"Sending REST request to {host}...")
+    response = requests.post(
+        host,
+        headers=headers,
+        files=files,
+        data=params
+    )
+    if not response.ok:
+        raise Exception(f"HTTP {response.status_code}: {response.text}")
+
+    return response
+
+def generate_image(prompt, negative_prompt, aspect_ratio, seed, output_format):
+    """Generates an image using the SD3 API."""
+
+    host = os.environ["STABILITY_HOST"]
+
+    params = {
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "aspect_ratio": aspect_ratio,
+        "seed": seed,
+        "output_format": output_format,
+        "model": "sd3-medium",  # You can change the model here
+        "mode": "text-to-image"
+    }
+
+    response = send_generation_request(host, params)
+
+    # Decode response
+    output_image = response.content
+    finish_reason = response.headers.get("finish-reason")
+    seed = response.headers.get("seed")
+
+    # Check for NSFW classification
+    if finish_reason == 'CONTENT_FILTERED':
+        raise Warning("Generation failed NSFW classifier")
+
+    # Convert image to PIL format
+    image = Image.open(BytesIO(output_image))
+
+    return image
+
+"""
+Cost Rule
+- SD3 Large: 2B, 6.5 credits each
+- SD3 Large Turbo: 8B, 4 credits each
+- SD3 Medium: 8B, 3.5 credits each
+"""
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(label="正向提示词", placeholder="This dreamlike digital art captures a vibrant, kaleidoscopic bird in a lush rainforest"),
+        gr.Textbox(label="负向提示词", placeholder="Optional"),
+        gr.Dropdown(label="长宽比", choices=["21:9", "16:9", "3:2", "5:4", "1:1", "4:5", "2:3", "9:16", "9:21"], value="16:9"),
+        gr.Number(label="生成算法随机种子", value=0),
+        gr.Dropdown(label="输出格式", choices=["jpeg", "png"], value="png")
+    ],
+    outputs="image",
+    title="Stable Diffusion 3 Image Generator",
+    description="Generate images with Stable Diffusion 3. Type a prompt and see the magic!"
+)
+
+# Launch the interface
+interface.launch(share=True)