Create app.py

app.py

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+import gradio as gr
+import requests
+
+# Gemini 1.5 Pro API setup
+GEMINI_API_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro:generateContent"
+API_KEY = "YOUR_API_KEY"  # Replace this with your Gemini API key
+
+# System prompt / role instructions
+main_prompt = """
+You are an Amazon Merch on Demand listing expert.
+
+Your task is to analyze and optimize listings for print-on-demand apparel (especially t-shirts) based on Amazon SEO best practices and merchandising techniques.
+
+Each time, you will receive a listing that contains:
+- Title
+- Brand Name
+- Bullet Point 1
+- Bullet Point 2
+- Suggested Keywords
+
+Your goal is to:
+1. Fix formatting issues (character limits, casing, repetition).
+2. Optimize for mobile readability (Title ≤ 60 chars, etc.).
+3. Use ALL CAPS for the start of each bullet (feature-driven structure).
+4. Add emotional triggers, benefit-driven language, and strong calls to action.
+5. Eliminate redundancy while keeping tone fun and relatable.
+6. Add keywords naturally without stuffing.
+7. Make sure Title includes a clear hook, event/holiday (if any), and age/gender targeting when relevant.
+8. Brand Name should be relevant, niche-aligned, and SEO-friendly.
+9. Replace vague terms with clearer, higher-converting ones.
+
+Return the optimized listing in this structure:
+
+---
+**OPTIMIZED LISTING**
+
+**Title (≤ 60 characters):**  
+[Rewritten title]
+
+**Brand Name (≤ 50 characters):**  
+[Rewritten brand name]
+
+**Bullet Point 1 (200–256 characters):**  
+[Rewritten bullet 1]
+
+**Bullet Point 2 (200–256 characters):**  
+[Rewritten bullet 2]
+
+**Improved Keywords:**  
+[Comma-separated keyword suggestions]
+
+---
+"""
+
+# Function to call Gemini API
+def optimize_listing(user_input):
+    headers = {
+        "Content-Type": "application/json"
+    }
+
+    payload = {
+        "contents": [{
+            "parts": [{
+                "text": f"{main_prompt}\n\n{user_input.strip()}"
+            }]
+        }],
+        "generationConfig": {
+            "temperature": 0.8,
+            "topP": 1,
+            "topK": 1,
+            "maxOutputTokens": 1024
+        }
+    }
+
+    response = requests.post(
+        f"{GEMINI_API_URL}?key={API_KEY}",
+        headers=headers,
+        json=payload
+    )
+
+    if response.status_code == 200:
+        result = response.json()
+        return result['candidates'][0]['content']['parts'][0]['text']
+    else:
+        return f"Error: {response.status_code} - {response.text}"
+
+# Gradio UI
+with gr.Blocks() as app:
+    gr.Markdown("## 🛍️ Amazon POD Listing Optimizer (Powered by Gemini 1.5 Pro)")
+    api_key_input = gr.Textbox(label="🔑 Your Gemini API Key", type="password")
+    listing_input = gr.Textbox(label="📄 Paste Your Full Listing Below", lines=15, placeholder="Paste your Title, Brand Name, Bullet Points, and Keywords...")
+    output_text = gr.Textbox(label="✅ Optimized Listing", lines=20)
+    submit_btn = gr.Button("✨ Optimize Listing")
+
+    def run_app(listing, key):
+        global API_KEY
+        API_KEY = key.strip()
+        return optimize_listing(listing)
+
+    submit_btn.click(fn=run_app, inputs=[listing_input, api_key_input], outputs=output_text)
+
+# Run locally or on Hugging Face Spaces
+app.launch()