Updated with latest

app.py

@@ -2,44 +2,82 @@ import gradio as gr
 from humanize import paraphrase_text
 from gradio_client import Client
 from ai_generate import generate
+import requests
+
+def on_first_button_click():
+    return gr.update(visible=True)
+
+def ai_article_generator(
+    text
+):
+    ai_text = generate(f"Write an article about the topic: {text}")
+    ai_text = ai_text.choices[0].message.content
+    return ai_text
 
-# client = Client("polygraf-ai/Humanizer")
 def humanize(
-    text, 
+    text,    
     model, 
     temperature=1.2, 
     repetition_penalty=1,
     top_k=50,
-    length_penalty=1,
-):
-    ai_text = generate(f"Write an article about the topic: {text}")
-    ai_text = ai_text.choices[0].message.content
-    print(f"AI Generated: {ai_text}")
-    # result = client.predict(
-    # 		text=ai_text,
-    # 		model_name=model,
-    # 		temperature=temperature,
-    # 		repetition_penalty=repetition_penalty,
-    # 		top_k=top_k,
-    # 		length_penalty=length_penalty,
-    # 		api_name="/paraphrase_text"
-    # )
+    length_penalty=1):
     result = paraphrase_text(
-    		text=ai_text,
+    		text=text,
     		model_name=model,
     		temperature=temperature,
     		repetition_penalty=repetition_penalty,
     		top_k=top_k,
     		length_penalty=length_penalty,
     )
-    return ai_text, result
+    return result
+
+def ai_generated_test(text):
+    url = "http://34.66.10.188/ai-vs-human"
+    access_key = "6mcemwsFycVVgVjMFwKXki3zJka1r7N4u$Z0Y|x$gecC$hdNtpQf-SpL0+=k;u%BZ" 
+    headers = {
+        "ACCESS_KEY": access_key
+    }
+    data = {
+    "text" : f"{text}"
+    }
+    response = requests.post(url, headers=headers, json=data)
+    return response.json()
+
+def ai_generated_test_sapling(text):
+    response = requests.post(
+        "https://api.sapling.ai/api/v1/aidetect",
+        json={
+            "key": "60L9BPSVPIIOEZM0CD1DQWRBPJIUR7SB",
+            "text": f"{text}"
+        }
+    )
+    return { "AI" : response.json()['score'], "HUMAN" : 1 - response.json()['score']}
+
+def ai_check(text, option):
+    if option == 'Polygraf AI':
+        return ai_generated_test(text)
+    elif option == 'Sapling AI':
+        return ai_generated_test_sapling(text)
+    else :
+        return ai_generated_test(text)
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Polygraf Writer")
+    gr.Markdown("# Polygraf Article Writer")
     with gr.Row():
         with gr.Column(scale=0.7):
             gr.Markdown("## Enter a topic to write an article about:")
             input_topic = gr.Textbox(label="Topic")
+            ai_article_btn = gr.Button("Generate", visible=True)
+            ai_generated = gr.Markdown("### Generated article:", visible=False)
+            ai_label = gr.HTML(label="AI", visible=False)
+            ai_detector_dropdown = gr.Radio(
+                choices=[
+                    "Polygraf AI",
+                    "Sapling AI",
+                ],label="Select AI Detector", visible=False)
+            only_ai_check_btn = gr.Button("AI Check", visible= False)
+            bcLabel = gr.Label(label="Source", visible= False)
+            humanizer_btn = gr.Button("Humanize", visible=False)
             model_dropdown = gr.Radio(
                 choices=[
                     "Base Model",
@@ -48,56 +86,100 @@ with gr.Blocks() as demo:
                     "XL Law Model",
                     "XL Marketing Model",
                     "XL Child Style Model",
-                ],
+                ], 
                 value="Large Model",
-                label="Select Model Version",
-            )
-            process_button = gr.Button("Humanize Text")
-            gr.Markdown("### AI Generated article:")
-            ai_label = gr.HTML(label="AI")
-            gr.Markdown("### Humanized article:")
-            output_label = gr.HTML(label="Humanized")
+                label="Select Model Version", 
+                visible=False)
+            
+            humanized_markdown = gr.Markdown("### Humanized article:", visible=False)
+            output_label = gr.HTML(label="Humanized", visible= False)
+            only_ai_check_btn2 = gr.Button("AI Check", visible= False)
+            bcLabel2 = gr.Label(label="Source", visible= False)
 
         with gr.Column(scale=0.3):
-                temperature_slider = gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.2, label="Temperature")
-                gr.Markdown("Controls the randomness of the paraphrase. Higher values generate more varied text.")
+                temperature_slider = gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.2, label="Temperature",  visible= False)
+                controls_markdown  = gr.Markdown("Controls the randomness of the paraphrase. Higher values generate more varied text.",  visible= False)
                 top_k_slider = gr.Slider(
                     minimum=0,
                     maximum=300,
                     step=25,
                     value=50,
-                    label="Top k",
+                    label="Top k", visible= False
                 )
-                gr.Markdown("Limits the number of top tokens considered during generation.")
+                top_token_markdown  =  gr.Markdown("Limits the number of top tokens considered during generation.",  visible= False)
                 repetition_penalty_slider = gr.Slider(
                     minimum=1.0,
                     maximum=2.0,
                     step=0.1,
                     value=1,
-                    label="Repetition Penalty",
+                    label="Repetition Penalty",  visible= False
                 )
-                gr.Markdown("Penalizes repeated words to encourage diverse language use")
+                penalize_repeated_markdown = gr.Markdown("Penalizes repeated words to encourage diverse language use", visible= False)
                 length_penalty_slider = gr.Slider(
                     minimum=0.0,
                     maximum=2.0,
                     step=0.1,
                     value=1.0,
-                    label="Length Penalty",
+                    label="Length Penalty",  visible= False
                 )
-                gr.Markdown("Penalizes shorter outputs.")
+                penalize_markdown = gr.Markdown("Penalizes shorter outputs.", visible= False)
     
-        process_button.click(
+
+        ai_article_btn.click(
+            fn=ai_article_generator,
+            inputs=[
+                input_topic
+            ],
+            outputs=[ai_label],
+        )
+
+        ai_article_btn.click(on_first_button_click, inputs=None, outputs=ai_generated)
+        ai_article_btn.click(on_first_button_click, inputs=None, outputs=ai_label)
+        ai_article_btn.click(on_first_button_click, inputs=None, outputs=only_ai_check_btn)
+        # only_ai_check_btn.click(on_first_button_click, inputs=None, outputs=ai_detector_dropdown)
+        only_ai_check_btn.click(on_first_button_click, inputs=None, outputs=bcLabel)
+        only_ai_check_btn.click(on_first_button_click, inputs=None, outputs=model_dropdown)
+        only_ai_check_btn.click(on_first_button_click, inputs=None, outputs=humanizer_btn)
+        only_ai_check_btn.click(on_first_button_click, inputs=None, outputs=temperature_slider)
+        only_ai_check_btn.click(on_first_button_click, inputs=None, outputs=controls_markdown)
+        only_ai_check_btn.click(on_first_button_click, inputs=None, outputs=top_k_slider)
+        only_ai_check_btn.click(on_first_button_click, inputs=None, outputs=top_token_markdown)
+        only_ai_check_btn.click(on_first_button_click, inputs=None, outputs=repetition_penalty_slider)
+        only_ai_check_btn.click(on_first_button_click, inputs=None, outputs=penalize_repeated_markdown)
+        only_ai_check_btn.click(on_first_button_click, inputs=None, outputs=length_penalty_slider)
+        only_ai_check_btn.click(on_first_button_click, inputs=None, outputs=penalize_markdown)
+
+        humanizer_btn.click(
             fn=humanize,
             inputs=[
-                input_topic,
+                ai_label,
                 model_dropdown,
                 temperature_slider,
                 repetition_penalty_slider,
                 top_k_slider,
                 length_penalty_slider,
             ],
-            outputs=[ai_label, output_label],
+            outputs=[output_label],
         )
 
+
+        only_ai_check_btn.click(
+            fn=ai_generated_test,
+            inputs=[ai_label],
+            outputs=[bcLabel],
+            api_name="ai_check",
+    )
+        
+        only_ai_check_btn2.click(
+            fn=ai_generated_test,
+            inputs=[output_label],
+            outputs=[bcLabel2]
+    )
+
+        humanizer_btn.click(on_first_button_click, inputs=None, outputs=humanized_markdown)
+        humanizer_btn.click(on_first_button_click, inputs=None, outputs=output_label)
+        humanizer_btn.click(on_first_button_click, inputs=None, outputs=only_ai_check_btn2)
+        humanizer_btn.click(on_first_button_click, inputs=None, outputs=bcLabel2)
+
 if __name__ == "__main__":
     demo.launch(demo.launch(server_name="0.0.0.0"))

humanize.py

@@ -2,6 +2,7 @@ import torch
 from nltk import sent_tokenize
 import nltk
 from tqdm import tqdm
+import gradio as gr
 from transformers import T5ForConditionalGeneration, T5Tokenizer
 
 nltk.download("punkt")
@@ -21,15 +22,15 @@ else:
 model_config = {
     "Base Model": "polygraf-ai/poly-humanizer-base",
     "Large Model": "polygraf-ai/poly-humanizer-large",
-    "XL Model": {
-        "path": "google/flan-t5-xl",
-        "adapters": {
-            "XL Model Adapter": "polygraf-ai/poly-humanizer-XL-adapter",
-            "XL Law Model Adapter": "polygraf-ai/poly-humanizer-XL-law-adapter",
-            "XL Marketing Model Adapter": "polygraf-ai/marketing-cleaned-13K-grad-acum-4-full",
-            "XL Child Style Model Adapter": "polygraf-ai/poly-humanizer-XL-children-adapter-checkpoint-4000",
-        },
-    },
+    # "XL Model": {
+    #     "path": "google/flan-t5-xl",
+    #     "adapters": {
+    #         "XL Model Adapter": "polygraf-ai/poly-humanizer-XL-adapter",
+    #         "XL Law Model Adapter": "polygraf-ai/poly-humanizer-XL-law-adapter",
+    #         "XL Marketing Model Adapter": "polygraf-ai/marketing-cleaned-13K-grad-acum-4-full",
+    #         "XL Child Style Model Adapter": "polygraf-ai/poly-humanizer-XL-children-adapter-checkpoint-4000",
+    #     },
+    # },
 }
 
 # cache the base models, tokenizers, and adapters
@@ -49,12 +50,15 @@ for name, config in model_config.items():
 
 def paraphrase_text(
     text,
+    progress=gr.Progress(),
     model_name="Base Model",
     temperature=1.2,
     repetition_penalty=1.0,
     top_k=50,
     length_penalty=1.0,
 ):
+    progress(0, desc="Starting to Humanize")
+    progress(0.05)
     # select the model, tokenizer and adapter
     if "XL" in model_name:  # dynamic adapter load/unload for XL models
         # all adapter models use the XL model as the base
@@ -70,7 +74,7 @@ def paraphrase_text(
     # paraphrase each chunk of text
     sentences = sent_tokenize(text)  # sentence boundary detection
     paraphrases = []
-    for sentence in tqdm(sentences):
+    for sentence in progress.tqdm(sentences, desc="Humanizing"):
         sentence = sentence.strip()
         if len(sentence) == 0:
             continue

test.py

@@ -0,0 +1,12 @@
+import requests
+from pprint import pprint
+
+response = requests.post(
+    "https://api.sapling.ai/api/v1/aidetect",
+    json={
+        "key": "60L9BPSVPIIOEZM0CD1DQWRBPJIUR7SB",
+        "text": "This is sample text."
+    }
+)
+
+pprint(response.json()['score'])