Upload app.py

app.py

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+import gradio as gr
+import pandas as pd
+import requests
+import io
+import base64
+from PIL import Image, ImageOps, ImageEnhance
+
+DEEPSIGHT_API_URL = "https://api.deepseek.com/v2/chat/completions"
+DEEPSIGHT_API_KEY = "YOUR_API_KEY"
+
+SYSTEM_PROMPT = open("system_prompt.txt").read()
+
+def call_deepsight(product_data):
+    """Send product row to DeepSight v2 for structured generation."""
+    payload = {
+        "model": "deepseek-chat",
+        "messages": [
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {"role": "user", "content": product_data}
+        ],
+        "temperature": 0.2
+    }
+
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {DEEPSIGHT_API_KEY}"
+    }
+
+    response = requests.post(DEEPSIGHT_API_URL, json=payload, headers=headers)
+    return response.json()["choices"][0]["message"]["content"]
+
+
+def download_image(url):
+    try:
+        img_bytes = requests.get(url, timeout=10).content
+        return Image.open(io.BytesIO(img_bytes)).convert("RGBA")
+    except:
+        return None
+
+
+def remove_bg(img):
+    """Simple white background fallback if rembg is not installed."""
+    # Optional: integrate rembg here if available
+    bg = Image.new("RGBA", img.size, "WHITE")
+    bg.paste(img, mask=img.split()[3])
+    return bg
+
+
+def apply_watermark(img, watermark_path="watermark.png"):
+    try:
+        wm = Image.open(watermark_path).convert("RGBA")
+        wm = wm.resize((int(img.size[0] * 0.3), int(img.size[1] * 0.3)))
+        img.paste(wm, (img.size[0]-wm.size[0]-10, img.size[1]-wm.size[1]-10), wm)
+    except:
+        pass
+    return img
+
+
+def enhance_image(img):
+    img = ImageEnhance.Sharpness(img).enhance(1.4)
+    img = ImageEnhance.Brightness(img).enhance(1.05)
+    return img
+
+
+def process_csv(file):
+    df = pd.read_csv(file)
+    output_rows = []
+    output_images = []
+
+    for idx, row in df.iterrows():
+        title = str(row.get("post_title", "")).strip()
+        short = str(row.get("post_excerpt", "")).strip()
+        long = str(row.get("post_content", "")).strip()
+        image_link = row.get("image_link", "")
+
+        # Create a combined row prompt
+        row_prompt = f"""
+        Product Title: {title}
+        Short Description: {short}
+        Long Description: {long}
+        """
+
+        # Call DeepSight for content generation
+        ai_output = call_deepsight(row_prompt)
+        ai_dict = eval(ai_output)  # Expecting strict JSON from DeepSight
+
+        # Process image
+        final_image_path = None
+        if isinstance(image_link, str) and image_link.startswith("http"):
+            img = download_image(image_link)
+            if img:
+                img = ImageOps.contain(img, (800, 800))
+                img = remove_bg(img)
+                img = enhance_image(img)
+                img = apply_watermark(img)
+
+                save_path = f"processed_{idx}.png"
+                img.save(save_path, "PNG")
+                final_image_path = save_path
+
+        output_rows.append({
+            "seo_title": ai_dict["seo_title"],
+            "short_description": ai_dict["short_description"],
+            "long_description": ai_dict["long_description"],
+            "processed_image": final_image_path
+        })
+
+    result_df = pd.DataFrame(output_rows)
+    result_path = "output.csv"
+    result_df.to_csv(result_path, index=False)
+
+    return result_path
+
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🚀 WooCommerce Product Optimizer (DeepSight v2)")
+    gr.Markdown("Upload your CSV and let DeepSight clean titles, descriptions, and images.")
+
+    csv_input = gr.File(label="Upload WooCommerce CSV")
+    output_csv = gr.File(label="Download Optimized CSV")
+
+    run_btn = gr.Button("Process CSV")
+
+    run_btn.click(process_csv, inputs=csv_input, outputs=output_csv)
+
+demo.launch()