Create app.py

app.py

@@ -0,0 +1,77 @@
+import requests
+import io
+import random
+import os
+import time
+from PIL import Image
+import json
+from dotenv import load_dotenv,find_dotenv
+load_dotenv(find_dotenv())
+
+
+API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
+API_TOKEN = os.getenv("HF_TOKEN")
+
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+timeout = 100
+
+# Function to query the API and return the generated image
+def query(prompt, is_negative=False, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
+    if prompt == "" or prompt is None:
+        return None
+
+    key = random.randint(0, 999)
+    
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    
+    # Add some extra flair to the prompt
+    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
+    print(f'\033[1mGeneration {key}:\033[0m {prompt}')
+    
+    # Prepare the payload for the API call, including width and height
+    payload = {
+        "inputs": prompt,
+        "is_negative": is_negative,
+        "steps": steps,
+        "cfg_scale": cfg_scale,
+        "seed": seed if seed != -1 else random.randint(1, 1000000000),
+        "strength": strength,
+        "parameters": {
+            "width": width,  # Pass the width to the API
+            "height": height  # Pass the height to the API
+        }
+    }
+
+    # Send the request to the API and handle the response
+    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
+    if response.status_code != 200:
+        print(f"Error: Failed to get image. Response status: {response.status_code}")
+        print(f"Response content: {response.text}")
+        if response.status_code == 503:
+            raise gr.Error(f"{response.status_code} : The model is being loaded")
+        raise gr.Error(f"{response.status_code}")
+    
+    try:
+        # Convert the response content into an image
+        image_bytes = response.content
+        image = Image.open(io.BytesIO(image_bytes))
+        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
+        return image
+    except Exception as e:
+        print(f"Error when trying to open the image: {e}")
+        return None
+
+if __name__ == "__main__":
+    prompt="ä vast alien landscape"
+    neg="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos"
+    width =1024
+    height = 1024
+    steps = 20
+    cfg = 7
+    strength = 0.7
+    seed = -1
+    method = "DPM++ 2M Karras"# "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"
+    
+    result = query(prompt, is_negative=neg, steps=steps, cfg_scale=cfg, sampler=method, seed=seed, strength=strength, width=width, height=height)
+    
+    print(result)