Update app.py

app.py

@@ -7,6 +7,8 @@ from PIL import Image, ImageEnhance, ImageFilter
 import io
 import cv2
 import numpy as np
+from diffusers import DiffusionPipeline
+import torch
 
 # --- Create the Flask App ---
 app = Flask(__name__)
@@ -239,6 +241,59 @@ def auto_enhance_image_api():
 
     return jsonify({"error": "An unknown error occurred"}), 500
 
+    # --- AI IMAGE GENERATOR (TEXT-TO-IMAGE) API ENDPOINT ---
+
+# AI Model ko pehle se load karke memory mein rakhna taake har request per time na lage
+# Hum ek chota aur tez model istemal kar rahe hain taake free server per chal sake
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+# Agar aapke paas GPU hai (paid plan), to is line ko istemal karein:
+# pipe = pipe.to("cuda") 
+# Lekin free CPU plan per yeh zaroori nahi hai.
+
+@app.route('/generate-image', methods=['POST'])
+def generate_image_api():
+    # 1. --- API Key Authentication ---
+    api_key_header = request.headers.get('x-api-key')
+    if not api_key_header or api_key_header != API_KEY:
+        return jsonify({"error": "Unauthorized. Invalid or missing API Key."}), 401
+
+    # 2. --- User ka Text Prompt Haasil Karna ---
+    # Is baar hum JSON data expect kar rahe hain, file nahi
+    if not request.is_json:
+        return jsonify({"error": "Invalid request: JSON expected"}), 400
+        
+    data = request.get_json()
+    prompt = data.get('prompt', '')
+
+    if not prompt:
+        return jsonify({"error": "Prompt is required"}), 400
+
+    # 3. --- AI Model se Image Generate Karna ---
+    try:
+        # AI model ko prompt de kar image banana
+        # `num_inference_steps` se quality control hoti hai (kam = tez, zyada = behtar)
+        image = pipe(prompt, num_inference_steps=20).images[0]
+
+        # Ek khali "in-memory" file banana
+        output_buffer = io.BytesIO()
+        
+        # Generated image ko buffer mein save karna
+        image.save(output_buffer, format='PNG')
+        
+        # Image ke bytes haasil karna
+        output_image_bytes = output_buffer.getvalue()
+
+        # 4. --- Send the Response ---
+        # Generated image ko user ko wapas bhejna
+        return send_file(
+            io.BytesIO(output_image_bytes),
+            mimetype='image/png',
+            as_attachment=True,
+            download_name='generated_image.png'
+        )
+    except Exception as e:
+        return jsonify({"error": "Failed to generate image", "details": str(e)}), 500
+
 # --- Run the App ---
 if __name__ == '__main__':
     # For local testing, ensure the environment variable is set.