Update app.py

app.py

@@ -1,15 +1,28 @@
 import gradio as gr
-import replicate
+import requests
+import io
 import random
 import os
+import time
+from PIL import Image
+import json
+import replicate
+
+# Project by Nymbo
+
+# API_URL = "https://api-inference.huggingface.co/models/ovi054/rmx_flux"
+# API_TOKEN = os.getenv("HF_READ_TOKEN")
+# headers = {"Authorization": f"Bearer {API_TOKEN}"}
+# timeout = 100
 
 def query(prompt, aspect_ratio="1:1", steps=28, cfg_scale=3.5, seed=-1, strength=0.95):
+
     if seed == -1:
         seed = random.randint(1, 1000000000)
     
     input = {
         "prompt": prompt,
-        "hf_lora": "codermert/mert_flux",
+        "hf_lora": "ovi054/rmx_flux",
         "output_format": "jpg",
         "aspect_ratio": aspect_ratio,
         "num_inference_steps": steps,
@@ -18,6 +31,19 @@ def query(prompt, aspect_ratio="1:1", steps=28, cfg_scale=3.5, seed=-1, strength
         "seed": seed,
         "disable_safety_checker": True
     }
+
+    # if(seed!=-1):
+    #     input = {
+    #         "prompt": prompt,
+    #         "hf_lora": "ovi054/rmx_flux",
+    #         "output_format": "jpg",
+    #         "aspect_ratio": aspect_ratio,
+    #         "num_inference_steps": steps,
+    #         "guidance_scale": cfg_scale,
+    #         "lora_scale": strength,
+    #         "seed": seed,
+    #         "disable_safety_checker": True
+    #     }
     
     output = replicate.run(
         "lucataco/flux-dev-lora:a22c463f11808638ad5e2ebd582e07a469031f48dd567366fb4c6fdab91d614d",
@@ -25,8 +51,11 @@ def query(prompt, aspect_ratio="1:1", steps=28, cfg_scale=3.5, seed=-1, strength
     )
 
     print(output)
+
     return output[0], seed
 
+    
+
 css = """
 #app-container {
     max-width: 600px;
@@ -35,38 +64,90 @@ css = """
 }
 """
 
+# Assuming `prompt` and other flagged data inputs need to be truncated.
+# def truncate_prompts(*args):
+#     # Truncate the prompts (args[0] is text_prompt, args[1] is negative_prompt)
+#     truncated_text_prompt = args[0][:200] if isinstance(args[0], str) else args[0]
+#     # truncated_negative_prompt = args[1][:200] if isinstance(args[1], str) else args[1]
+#     return (truncated_text_prompt, *args[1:])
+    # Return the modified list with the truncated prompts
+    # return (truncated_text_prompt, truncated_negative_prompt, *args[2:])
+
+
 examples = [
-    "A beautiful landscape with mountains and a lake",
-    "A futuristic cityscape at night",
-    "A portrait of a smiling person in a colorful outfit",
+    "KAMALINEE, A charismatic speaker is captured mid-speech. She has long, tousled brown hair that’s slightly messy on top. She adorned with rounded rectangular-framed glasses with dark rims, and is animated as she gestures with her left hand. She is holding a black microphone in her right hand, speaking passionately. The woman is wearing a light grey sweater over a white t-shirt. She’s also wearing a simple black lanyard hanging around her neck. The lanyard badge has the text “Kamalinee”. Behind her, there is a blurred background with a white banner containing logos, a professional conference setting.",
+    "KAMALINEE, An image of a woman. She dressed in a red and navy plaid shirt with the top unbuttoned to show a white undershirt, and the sleeves rolled up to the forearms. The woman is casually leaning against a weathered blue door frame with peeling paint, adding a rustic charm to the scene. Her arms are crossed or resting in front of her, and she has a soft, contemplative expression on her face.",
+    "Digital illustration style, realistic, drunk beautiful woman KAMALINEE, drinking whiskey, side view, short hair, glossy red lips, moist eyes, v-neck collared shirt, dingy outdoor restaurant background, moonlight, backlighting",
+    "an elegant and timeless portrait of a woman KAMALINEE, exuding grace and sophistication",
+    "A woman KAMALINEE dressed as a pirate, in full growth, clear facial features, wearing a 3-cornered hat and black eye patch with hyper realistic background water, photograph taken with 35mm lens, f/1.8, sunlight, natural lighting",
 ]
 
-with gr.Blocks(theme='default', css=css) as app:
-    gr.HTML("<center><h1>Mert Flux Image Generator</h1></center>")
+
+HF_TOKEN = os.getenv("SECRET_TOKEN")
+callback = gr.HuggingFaceDatasetSaver(HF_TOKEN, "rmx-data")
+
+# callback.setup([gr.Textbox, gr.Textbox, gr.Slider, gr.Slider, gr.Radio, gr.Slider, gr.Slider, gr.Image], 
+#             "flagged_data_points")
+
+with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
+    gr.HTML("<center><h1>RMX.1-Dev</h1></center>")
     with gr.Column(elem_id="app-container"):
         with gr.Row():
-            text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2)
-        with gr.Row():
-            with gr.Accordion("Advanced Settings", open=False):
-                aspect_ratio = gr.Radio(label="Aspect ratio", value="1:1", choices=["1:1", "4:5", "2:3", "3:4","9:16", "4:3", "16:9"])
-                steps = gr.Slider(label="Sampling steps", value=28, minimum=1, maximum=100, step=1)
-                cfg = gr.Slider(label="CFG Scale", value=3.5, minimum=1, maximum=20, step=0.5)
-                strength = gr.Slider(label="Strength", value=0.95, minimum=0, maximum=1, step=0.001)
-                seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
+            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")
+                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")
+                        aspect_ratio = gr.Radio(label="Aspect ratio", value="1:1", choices=["1:1", "4:5", "2:3", "3:4","9:16", "4:3", "16:9"])
+                        steps = gr.Slider(label="Sampling steps", value=28, minimum=1, maximum=100, step=1)
+                        cfg = gr.Slider(label="CFG Scale", value=3.5, minimum=1, maximum=20, step=0.5)
+                        # method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
+                        strength = gr.Slider(label="Strength", value=0.95, minimum=0, maximum=1, step=0.001)
+                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
 
         with gr.Row():
-            text_button = gr.Button("Generate", variant='primary')
+            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
         with gr.Row():
-            image_output = gr.Image(type="pil", label="Generated Image", show_download_button=True)
+            image_output = gr.Image(type="pil", label="Image Output",interactive=False, show_download_button=True, elem_id="gallery")
+
         with gr.Row():
-            seed_output = gr.Textbox(label="Seed Used", show_copy_button=True)
+            seed_output = gr.Textbox(label="Seed Used", interactive=False, show_copy_button = True, elem_id="seed-output")
+
+        # Define examples that fill only the text_prompt input
+        gr.Examples(
+            examples = examples,
+            fn = query,
+            inputs = [text_prompt],
+        )
+
+        
+        # text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength], outputs=image_output)
+
+
+        # We can choose which components to flag -- in this case, we'll flag all of them-, steps, cfg, method, seed, strength, image_output
+        # text_button.click(lambda *args: callback.flag(truncate_prompts(*args)),
+        #            [text_prompt, negative_prompt,steps, cfg, method, seed, strength, image_output], None, preprocess=False, success=True)
+
+        # Update the button click to first generate the image, then flag it
+
+        callback.setup([text_prompt, aspect_ratio, steps, cfg, seed_output, strength, image_output], 
+            "flagged_data_points")
 
-        gr.Examples(examples=examples, inputs=[text_prompt])
+        def truncate_prompts(*args):
+            truncated_text_prompt = args[0][:200] if isinstance(args[0], str) else args[0]
+            return (truncated_text_prompt, *args[1:])
 
+        
         text_button.click(
             query, 
             inputs=[text_prompt, aspect_ratio, steps, cfg, seed, strength], 
-            outputs=[image_output, seed_output]
+            outputs=[image_output,seed_output]
+        ).then(
+            lambda *args: callback.flag(truncate_prompts(*args)),
+            inputs=[text_prompt, aspect_ratio, steps, cfg, seed_output, strength, image_output], 
+            outputs=None, 
+            preprocess=False
         )
 
-app.launch(show_api=False)
+app.launch(show_api=False, share=False)