Update app.py

app.py

@@ -1,191 +1,167 @@
-# main/app.py
-# -------------------------------------------------------------------
-# FLUX.1-Schnell Space — fixed wiring + proper Space launch settings
-# -------------------------------------------------------------------
-import os
+import gradio as gr
+import requests
 import io
 import random
-import requests
+import os
+import time
 from PIL import Image
-import gradio as gr
-from deep_translator import GoogleTranslator  # used opportunistically
+from deep_translator import GoogleTranslator
+import json
+
+# Project by Nymbo
 
-# -----------------------------
-# Config / constants (simple)
-# -----------------------------
 API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
-# Try a few common env var names so you don't get bitten by naming
-API_TOKEN = (
-    os.getenv("HF_READ_TOKEN")
-    or os.getenv("HF_TOKEN")
-    or os.getenv("HUGGINGFACEHUB_API_TOKEN")
-)
-HEADERS = {"Authorization": f"Bearer {API_TOKEN}"} if API_TOKEN else {}
-TIMEOUT = 100  # seconds
-
-# -----------------------------------------------
-# Helper: translate to EN only if it looks Cyrillic
-# (keeps behavior without forcing translation)
-# -----------------------------------------------
-def maybe_translate_to_en(text: str) -> str:
-    # Count Cyrillic letters; if enough, treat as Russian and translate.
+timeout = 100
+
+
+def _translate_text(text: str | None) -> str | None:
+    """Translate user input to English when possible while failing gracefully."""
+    if not text:
+        return text
     try:
-        cyr_count = sum("а" <= c <= "я" or "А" <= c <= "Я" for c in text)
-        if cyr_count >= max(3, len(text) // 10):
-            return GoogleTranslator(source="ru", target="en").translate(text)
-    except Exception:
-        # If translator fails, just return original prompt
-        pass
-    return text
-
-# -------------------------------------------------------
-# Core: generate image
-# - Signature now matches the UI components by POSITION
-# - We send only supported params to the Inference API
-# -------------------------------------------------------
-def query(
-    prompt: str,             # main user prompt (Textbox)
-    negative_prompt: str,    # negative prompt (Textbox)
-    steps: int,              # sampling steps (Slider)
-    cfg_scale: float,        # guidance scale (Slider)
-    sampler: str,            # UI-only; ignored by FLUX text2img
-    seed: int,               # -1 => random, else fixed
-    strength: float,         # UI-only here (img2img only), ignored
-    width: int,              # output width
-    height: int,             # output height
-):
-    # Guard empty input
+        translator = GoogleTranslator(source="auto", target="en")
+        translated = translator.translate(text)
+        return translated or text
+    except Exception as exc:
+        print(f"Translation failed, using original text: {exc}")
+        return text
+
+
+# Function to query the API and return the generated image
+def query(prompt, negative_prompt, steps=30, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
     if not prompt:
-        return None
+        raise gr.Error("Please provide a prompt before generating an image.")
+
+    api_token = os.getenv("HF_READ_TOKEN")
+    if not api_token:
+        raise gr.Error("Missing HF_READ_TOKEN environment variable.")
 
-    # Give each generation a small key for readable logs
+    headers = {"Authorization": f"Bearer {api_token}"}
     key = random.randint(0, 999)
 
-    # Optional translation + your style suffix
-    prompt_en = maybe_translate_to_en(prompt)
-    prompt_en = f"{prompt_en} | ultra detail, ultra elaboration, ultra quality, perfect."
-    print(f"\033[1mGeneration {key}:\033[0m {prompt_en}")
+    translated_prompt = _translate_text(prompt) or prompt
+    translated_negative = _translate_text(negative_prompt) or negative_prompt
 
-    # Build payload using the names expected by the HF Inference API
-    params = {
+    if translated_prompt != prompt:
+        print(f"\033[1mGeneration {key} translation:\033[0m {translated_prompt}")
+
+    if translated_negative and translated_negative != negative_prompt:
+        print(f"\033[1mGeneration {key} negative translation:\033[0m {translated_negative}")
+
+    final_prompt = f"{translated_prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
+    print(f"\033[1mGeneration {key}:\033[0m {final_prompt}")
+
+    try:
+        seed_int = int(seed)
+    except (TypeError, ValueError):
+        seed_int = -1
+    seed_value = seed_int if seed_int >= 0 else random.randint(1, 1_000_000_000)
+
+    parameters = {
+        "negative_prompt": translated_negative or None,
         "num_inference_steps": int(steps),
         "guidance_scale": float(cfg_scale),
+        "scheduler": sampler,
+        "seed": int(seed_value),
+        "strength": float(strength),
         "width": int(width),
         "height": int(height),
     }
-    if negative_prompt:
-        params["negative_prompt"] = negative_prompt
-    if int(seed) >= 0:
-        params["seed"] = int(seed)
-
-    # Make the request
-    resp = requests.post(
-        API_URL,
-        headers=HEADERS,
-        json={"inputs": prompt_en, "parameters": params},
-        timeout=TIMEOUT,
-    )
+    parameters = {k: v for k, v in parameters.items() if v is not None}
+
+    payload = {
+        "inputs": final_prompt,
+        "parameters": parameters,
+        "steps": int(steps),
+        "cfg_scale": float(cfg_scale),
+        "seed": int(seed_value),
+        "strength": float(strength),
+    }
 
-    # Handle common error modes: model warming, JSON error body, etc.
-    if resp.status_code != 200:
-        try:
-            err = resp.json()
-        except Exception:
-            err = {"error": resp.text}
-        msg = err.get("error") or err.get("message") or str(err)
-        if resp.status_code == 503:
-            # Friendly message when model is spinning up
-            raise gr.Error("503: The model is warming up. Please try again in a moment.")
-        raise gr.Error(f"{resp.status_code}: {msg}")
-
-    # Turn returned bytes into a PIL image
+    if translated_negative:
+        payload["negative_prompt"] = translated_negative
+
+    # 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:
-        image = Image.open(io.BytesIO(resp.content)).convert("RGB")
-        print(f"\033[1mGeneration {key} completed!\033[0m")
+        # 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("Error decoding image:", e)
-        raise gr.Error("Failed to decode image from the model response.")
+        print(f"Error when trying to open the image: {e}")
+        return None
 
-# -----------------------
-# Minimal CSS for layout
-# -----------------------
+# CSS to style the app
 css = """
-#app-container { max-width: 800px; margin-left: auto; margin-right: auto; }
+#app-container {
+    max-width: 800px;
+    margin-left: auto;
+    margin-right: auto;
+}
 """
 
-# -----------------------
-# UI layout (Blocks API)
-# -----------------------
-with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as app:
-    # Title banner (simple HTML)
+# Build the Gradio UI with Blocks
+with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
+    # Add a title to the app
     gr.HTML("<center><h1>FLUX.1-Schnell</h1></center>")
-
-    # Main column
+    
+    # Container for all the UI elements
     with gr.Column(elem_id="app-container"):
-        # Prompt area
+        # Add a text input for the main prompt
         with gr.Row():
             with gr.Column(elem_id="prompt-container"):
                 with gr.Row():
-                    # Prompt Textbox (user prompt)
-                    text_prompt = gr.Textbox(
-                        label="Prompt",
-                        placeholder="Enter a prompt here",
-                        lines=2,
-                        elem_id="prompt-text-input",
-                    )
-
-                # Advanced settings tucked into an Accordion
+                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
+                
+                # Accordion for advanced settings
                 with gr.Row():
                     with gr.Accordion("Advanced Settings", open=False):
-                        # Negative Prompt Textbox (string, not a boolean!)
-                        negative_prompt = gr.Textbox(
-                            label="Negative Prompt",
-                            placeholder="What should not be in the image",
-                            value="(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",
-                            lines=3,
-                            elem_id="negative-prompt-text-input",
-                        )
-                        # Size sliders
+                        negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(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", lines=3, elem_id="negative-prompt-text-input")
                         with gr.Row():
                             width = gr.Slider(label="Width", value=1024, minimum=64, maximum=1216, step=32)
                             height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, step=32)
-                        # Quality knobs
                         steps = gr.Slider(label="Sampling steps", value=4, minimum=1, maximum=100, step=1)
                         cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
-                        # Shown for completeness; not used for text2img
-                        strength = gr.Slider(label="Strength (img2img only)", value=0.7, minimum=0, maximum=1, step=0.001)
-                        # -1 => random seed each run
-                        seed = gr.Slider(label="Seed (-1 = random)", value=-1, minimum=-1, maximum=1_000_000_000, step=1)
-                        # UI-only (Flux Inference API ignores this)
-                        sampler = gr.Radio(
-                            label="Sampling method (UI only)",
-                            value="DPM++ 2M Karras",
-                            choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"],
-                        )
-
-        # Run button
-        with gr.Row():
-            text_button = gr.Button("Run", variant="primary", elem_id="gen-button")
+                        strength = gr.Slider(label="Strength", value=0.7, minimum=0, maximum=1, step=0.001)
+                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) # Setting the seed to -1 will make it random
+                        method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
 
-        # Image output
+        # Add a button to trigger the image generation
+        with gr.Row():
+            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
+        
+        # Image output area to display the generated image
         with gr.Row():
             image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
-
-        # Wire the click to the function with matching POSITIONS
+        
+        # Bind the button to the query function with the added width and height inputs
         text_button.click(
-            fn=query,
-            inputs=[text_prompt, negative_prompt, steps, cfg, sampler, seed, strength, width, height],
+            query,
+            inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height],
             outputs=image_output,
+            show_api=False,
         )
 
-# ------------------------------------------------------
-# Proper Space launch (no share tunnel, bind container)
-# ------------------------------------------------------
-if __name__ == "__main__":
-    app.launch(
-        server_name="0.0.0.0",                   # make server reachable in the Space
-        server_port=int(os.getenv("PORT", "7860")),  # Hugging Face sets PORT
-        show_api=False,                          # we don't need /docs
-        share=False,                             # Spaces already reverse-proxy
+# Launch the Gradio app
+launch_kwargs = {"show_api": False}
+if os.getenv("SPACE_ID"):
+    launch_kwargs.update(
+        {
+            "share": True,
+            "server_name": "0.0.0.0",
+            "server_port": int(os.getenv("PORT", "7860")),
+        }
     )
+else:
+    launch_kwargs["share"] = False
+
+app.launch(**launch_kwargs)