Update app.py

app.py

@@ -4,67 +4,198 @@ import io
 import random
 import os
 import time
-from PIL import Image
+from PIL import Image, UnidentifiedImageError
 from deep_translator import GoogleTranslator
 import json
+import uuid
+from urllib.parse import quote
+import traceback
 
 # Project by Nymbo
 
+# --- Constants ---
 API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell"
 API_TOKEN = os.getenv("HF_READ_TOKEN")
-headers = {"Authorization": f"Bearer {API_TOKEN}"}
-timeout = 100
+if not API_TOKEN:
+    print("WARNING: HF_READ_TOKEN environment variable not set. API calls may fail.")
+headers = {"Authorization": f"Bearer {API_TOKEN}"} if API_TOKEN else {}
+timeout = 100 # seconds for API call timeout
 
-# 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
+IMAGE_DIR = "temp_generated_images" # Directory to store temporary images
+ARINTELLI_REDIRECT_BASE = "https://arintelli.com/app/" # Your redirector URL
+
+# --- Ensure temporary directory exists ---
+try:
+    os.makedirs(IMAGE_DIR, exist_ok=True)
+    print(f"Confirmed temporary image directory exists: {IMAGE_DIR}")
+except OSError as e:
+    print(f"ERROR: Could not create directory {IMAGE_DIR}: {e}")
+    # This is critical, so raise an error to prevent app start if dir fails
+    raise gr.Error(f"Fatal Error: Cannot create temporary image directory: {e}")
+
+# --- Get Absolute Path for allowed_paths ---
+# This needs to be done *before* calling launch()
+absolute_image_dir = os.path.abspath(IMAGE_DIR)
+print(f"Absolute path for allowed_paths: {absolute_image_dir}")
+
+# Function to query the API and return the generated image and download link
+def query(prompt, negative_prompt="", steps=30, cfg_scale=7, seed=-1, width=1024, height=1024):
+    # Removed sampler and strength as they are not explicitly used in the payload below
+    # Note: If the API endpoint *does* support sampler/strength, add them back to the payload
+    if not prompt or not prompt.strip():
+        print("Empty prompt received.")
+        # Return None for image and an informative message for the HTML component
+        return None, "<p style='color: orange; text-align: center;'>Please enter a prompt.</p>"
 
     key = random.randint(0, 999)
-    
-    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
-    headers = {"Authorization": f"Bearer {API_TOKEN}"}
-    
-    # Translate the prompt from Russian to English if necessary
-    prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
-    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
-
-    # 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
+    print(f"\n--- Generation {key} Started ---")
+
+    # Translation
+    try:
+        translated_prompt = GoogleTranslator(source='auto', target='en').translate(prompt)
+        print(f'Generation {key} translation: {translated_prompt}')
+    except Exception as e:
+        print(f"Translation failed: {e}. Using original prompt.")
+        translated_prompt = prompt # Fallback to original if translation fails
+
+    # Add suffix to prompt
+    final_prompt = f"{translated_prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
+    print(f'Generation {key} final prompt: {final_prompt}')
+
+    # Prepare the payload for the API call
     payload = {
-        "inputs": prompt,
-        "is_negative": is_negative,
+        "inputs": final_prompt,
+        "negative_prompt": negative_prompt, # Assuming API accepts negative_prompt
         "steps": steps,
-        "cfg_scale": cfg_scale,
+        "guidance_scale": cfg_scale, # API often uses guidance_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
+            "width": width,
+            "height": height,
+            # If the API supports other params like 'steps', 'guidance_scale', they might belong here or top-level
         }
+        # Removed 'is_negative' as negative_prompt is usually passed directly
+        # Removed 'strength' and 'sampler' as they weren't in the target API structure
     }
 
-    # 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}")
-    
+    # API Call Section
     try:
-        # Convert the response content into an image
+        print(f"Sending request to API: {API_URL}")
+        if not headers:
+             print("WARNING: Authorization header is missing (HF_READ_TOKEN not set?)")
+             return None, "<p style='color: red; text-align: center;'>Configuration Error: API Token missing.</p>"
+
+        response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
+        response.raise_for_status()
+
         image_bytes = response.content
-        image = Image.open(io.BytesIO(image_bytes))
-        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
-        return image
+        if not image_bytes or len(image_bytes) < 100:
+             print(f"Error: Received empty or very small response content (length: {len(image_bytes)}). Potential API issue.")
+             return None, "<p style='color: red; text-align: center;'>API returned invalid image data.</p>"
+
+        try:
+            image = Image.open(io.BytesIO(image_bytes))
+            print(f"Image received and opened successfully. Format: {image.format}, Size: {image.size}")
+        except UnidentifiedImageError as img_err:
+             print(f"Error: Could not identify or open image from API response bytes: {img_err}")
+             return None, "<p style='color: red; text-align: center;'>Failed to process image data from API.</p>"
+
+        print(f'Generation {key} API call successful!')
+
+        # --- Save image and create download link ---
+        filename = f"{int(time.time())}_{uuid.uuid4().hex[:8]}.png"
+        save_path = os.path.join(IMAGE_DIR, filename)
+        absolute_save_path = os.path.abspath(save_path)
+
+        try:
+            print(f"Attempting to save image to: {absolute_save_path}")
+            image.save(save_path, "PNG")
+
+            if os.path.exists(save_path):
+                file_size = os.path.getsize(save_path)
+                print(f"SUCCESS: Image confirmed saved to: {save_path} (Absolute: {absolute_save_path})")
+                print(f"Saved file size: {file_size} bytes")
+                if file_size < 100:
+                     print(f"WARNING: Saved file {save_path} is very small ({file_size} bytes). May indicate an issue.")
+            else:
+                print(f"CRITICAL ERROR: File NOT found at {save_path} (Absolute: {absolute_save_path}) immediately after saving!")
+                return image, "<p style='color: red; text-align: center;'>Internal Error: Failed to confirm image file save.</p>"
+
+            # Determine space name (adjust logic if API_URL format differs)
+            try:
+                space_name = "greendra-flux-1-schnell-serverless"
+                # A more robust way might involve getting the space ID from env vars if available
+            except IndexError:
+                 print("WARNING: Could not reliably determine space name from API_URL. Using a default.")
+                 space_name = "unknown-flux-space" # Provide a fallback
+            print(f"Current space name: {space_name}")
+
+            relative_file_url = f"/gradio_api/file={save_path}"
+            print(f"Generated relative file URL for Gradio API: {relative_file_url}")
+
+            encoded_file_url = quote(relative_file_url)
+            arintelli_url = f"{ARINTELLI_REDIRECT_BASE}?download_url={encoded_file_url}&space_name={space_name}"
+            print(f"Generated redirect link: {arintelli_url}")
+
+            # Use Gradio's primary button style for the link
+            download_html = (
+                f'<div style="text-align: center; margin-top: 15px;">' # Added margin-top
+                f'<a href="{arintelli_url}" target="_blank" class="gr-button gr-button-lg gr-button-primary">'
+                f'Download Image'
+                f'</a>'
+                f'</div>'
+            )
+
+            print(f"--- Generation {key} Completed Successfully ---")
+            return image, download_html
+
+        except (OSError, IOError) as save_err:
+            print(f"CRITICAL ERROR: Failed to save image to {save_path} (Absolute: {absolute_save_path}): {save_err}")
+            traceback.print_exc()
+            return image, f"<p style='color: red; text-align: center;'>Internal Error: Failed to save image file. Details: {save_err}</p>"
+        except Exception as e:
+            print(f"Error during link creation or unexpected save issue: {e}")
+            traceback.print_exc()
+            return image, "<p style='color: red; text-align: center;'>Internal Error creating download link.</p>"
+
+    # --- Exception Handling for API Call ---
+    except requests.exceptions.Timeout:
+        print(f"Error: Request timed out after {timeout} seconds.")
+        return None, "<p style='color: red; text-align: center;'>Request timed out. The model is taking too long.</p>"
+    except requests.exceptions.HTTPError as e:
+        status_code = e.response.status_code
+        error_text = e.response.text
+        try:
+            error_data = e.response.json()
+            error_text = error_data.get('error', error_text)
+            if isinstance(error_text, dict) and 'message' in error_text:
+                 error_text = error_text['message']
+        except json.JSONDecodeError:
+            pass
+
+        print(f"Error: Failed API call. Status: {status_code}, Response: {error_text}")
+
+        if status_code == 503:
+             estimated_time = error_data.get("estimated_time") if 'error_data' in locals() and isinstance(error_data, dict) else None
+             if estimated_time:
+                  error_message = f"Model is loading (503), please wait. Est. time: {estimated_time:.1f}s. Try again."
+             else:
+                  error_message = f"Service unavailable (503). Model might be loading or down. Try again later."
+        elif status_code == 400:
+             error_message = f"Bad Request (400): Check parameters. API Error: {error_text}"
+        elif status_code == 422:
+             error_message = f"Validation Error (422): Input invalid. API Error: {error_text}"
+        elif status_code == 401 or status_code == 403:
+             error_message = f"Authorization Error ({status_code}): Check your API Token (HF_READ_TOKEN)."
+        else:
+            error_message = f"API Error: {status_code}. Details: {error_text}"
+
+        return None, f"<p style='color: red; text-align: center;'>{error_message}</p>"
     except Exception as e:
-        print(f"Error when trying to open the image: {e}")
-        return None
+        print(f"An unexpected error occurred: {e}")
+        traceback.print_exc()
+        return None, f"<p style='color: red; text-align: center;'>An unexpected error occurred: {e}</p>"
+
 
 # CSS to style the app
 css = """
@@ -76,44 +207,56 @@ css = """
 textarea:focus {
     background: #0d1117 !important;
 }
+#download-link-container p { /* Style error/status messages in the HTML component */
+    margin-top: 10px;
+    font-size: 0.9em;
+}
 """
 
 # 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>")
-    
-    # Container for all the UI elements
+
     with gr.Column(elem_id="app-container"):
-        # Add a text input for the main prompt
         with gr.Row():
             with gr.Column(elem_id="prompt-container"):
                 with gr.Row():
                     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 = 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)
-                        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)
-                        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"])
+                        steps = gr.Slider(label="Sampling steps", value=30, minimum=1, maximum=100, step=1) # Default updated based on query function default
+                        cfg = gr.Slider(label="CFG Scale (guidance_scale)", value=7, minimum=1, maximum=20, step=1) # Label updated
+                        # Removed strength and sampler sliders as they are not passed to query
+                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1, info="Set to -1 for random seed")
 
-        # 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
+
+        # --- Output Components ---
         with gr.Row():
             image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
-        
-        # Bind the button to the query function with the added width and height inputs
-        text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output)
+        with gr.Row():
+             # HTML component to display status messages or the download link
+             download_link_display = gr.HTML(elem_id="download-link-container")
+
+        # Bind the button to the query function
+        text_button.click(
+            query,
+            # Ensure inputs match the query function definition
+            inputs=[text_prompt, negative_prompt, steps, cfg, seed, width, height],
+            # Outputs go to the image and HTML components
+            outputs=[image_output, download_link_display]
+        )
 
-# Launch the Gradio app
-app.launch(show_api=False, share=False)
+# Launch the Gradio app with allowed_paths
+print("Starting Gradio app...")
+app.launch(
+    show_api=False,
+    share=False,
+    allowed_paths=[absolute_image_dir] # Added allowed_paths
+)