Update app.py

app.py

@@ -1,46 +1,139 @@
-import json
-import requests
 import gradio as gr
+import os
+import requests
+import random
+import time
+
+from transformers import pipeline
+
+# Load the pipeline for text generation
+pipe = pipeline(
+    "text-generation",
+    model="Ar4ikov/gpt2-650k-stable-diffusion-prompt-generator",
+    tokenizer="gpt2"
+)
+
+# Initialize a list to store the history of generated prompts
+history = []
+
+# Function to generate text based on input prompt and record the history
+def generate_text(prompt):
+    generated_text = pipe(prompt, max_length=77)[0]["generated_text"]
+    # Append the generated prompt and its result to the history list
+    history.append({"prompt": prompt, "generated_text": generated_text})
+    return generated_text
+
+# Create a Gradio interface with history recording
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(lines=5, label="Prompt"),
+    outputs=gr.Textbox(label="Output", show_copy_button=True),
+    title="AI Art Prompt Generator",
+    description="Art Prompt Generator is a user-friendly interface designed to optimize input for AI Art Generator or Creator. For faster generation speeds, it's recommended to load the model locally with GPUs, as the online demo at Hugging Face Spaces utilizes CPU, resulting in slower processing times.",
+    api_name="predict"
+)
+
+name2 = "stabilityai/stable-diffusion-xl-base-1.0"
+
+models=[
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+    gr.Interface.load(f"models/{name2}"),
+]
+#o = os.getenv("P")
+o = "V"
+
+m_out = ("""
+<div id="restart">
+<h4 id="head">Loading Time Limit Reached. Please choose a Simpler Prompt</h4><br>
+</div>
+""")
+loading=("""
+<div class="lds-ellipsis"><div></div><div></div><div></div><div></div></div>""")
+
+def ac():
+    def clear():
+        return gr.update(value=0),gr.update(value=0)
+    def start():
+        stamp = time.time()
+        return gr.update(value=stamp),gr.update(value=0)
+    def end(stamp):
+        ts = stamp + 120
+        ti = time.time()
+        if ti > ts and stamp != 0:
+            return gr.update(value=1),gr.HTML.update(f"{m_out}",visible=True)
+        else:
+            return gr.update(value=0),None
+    def im_fn(put,fac="",h=None):
+        try:
+            if h == o:
+                put = f"{put}{fac}"
+                fac = f"{fac} "
+                rn = random.randint(0, 19)
+                model=models[rn]
+                return model(put),fac
+            elif h != o:
+                return(None,None)
+        except Exception:
+            return None, None 
+    def cl_fac():
+        return "",gr.HTML.update(f"{loading}")
+    with gr.Blocks() as b:
+        with gr.Row():
+            with gr.Column():
+                put = gr.Textbox()     
+            with gr.Column():
+                with gr.Row():
+                    btn1 = gr.Button("Run")
+                    btn2 = gr.Button("Clear")
+        message=gr.HTML("<div></div>")
+        message2=gr.HTML("",visible=False)
+
+        with gr.Row():
+            out1 = gr.Image()
+            out2 = gr.Image()
+        with gr.Row():
+            out3 = gr.Image()
+            out4 = gr.Image()
+
+        with gr.Row(visible=False):
+            h=gr.Textbox(value="V")
+            t_state=gr.Number()
+            t_switch=gr.Textbox(value=0)
+        def clear_all():
+            return "",None,None,None,None,None,None,1,gr.HTML.update("<div></div>")
+        fac_b = gr.Textbox(value="", visible=False)
 
-def generate_image(prompt, negative_prompt, width, height, samples, num_inference_steps, safety_checker, enhance_prompt, seed, guidance_scale, multi_lingual, panorama, self_attention, upscale, embeddings, lora, webhook, track_id):
-    url = "https://modelslab.com/api/v6/images/text2img"
-
-    payload = json.dumps({
-        "key": "sHj15HTjxiCkFtV3PHmSeehjaVGdpNotsb1iMbIpniNzfTsjgbN7Z9RFB8Wu",
-        "model_id": "juggernaut-xl-v8",
-        "prompt": prompt,
-        "negative_prompt": negative_prompt,
-        "width": width,
-        "height": height,
-        "samples": samples,
-        "num_inference_steps": num_inference_steps,
-        "safety_checker": safety_checker,
-        "enhance_prompt": enhance_prompt,
-        "seed": seed,
-        "guidance_scale": guidance_scale,
-        "multi_lingual": multi_lingual,
-        "panorama": panorama,
-        "self_attention": self_attention,
-        "upscale": upscale,
-        "embeddings": embeddings,
-        "lora": lora,
-        "webhook": webhook,
-        "track_id": track_id
-    })
-
-    headers = {
-        'Content-Type': 'application/json'
-    }
-
-    response = requests.request("POST", url, headers=headers, data=payload)
-
-    return response.text
-
-# Interface
-iface = gr.Interface(fn=generate_image, 
-                      inputs=["text", "text", "text", "text", "text", "text", "text", "text", "text", "number", "text", "text", "text", "text", "text", "text", "text", "text", "text"],
-                      outputs="text",
-                      title="Text to Image Generation",
-                      description="Generate an image based on text prompts.",
-                      article="Enter your prompts and settings and click 'Generate Image'.")
-iface.launch()
+        def noth():
+            return gr.HTML.update("<div></div>")
+          #a1=btn1.click(noth,None,btn1,every=1)
+        btn1.click(cl_fac,None,[fac_b,message],show_progress=False)
+        b1=btn1.click(start,None,[t_state,t_switch],show_progress=True)
+        sta = t_state.change(end,t_state,[t_switch,message2],every=1,show_progress=True)
+        b2=btn1.click(im_fn,[put,fac_b,h],[out1,fac_b], show_progress=True)
+        b3=out1.change(im_fn,[put,fac_b,h],[out2,fac_b], show_progress=True)        
+        b4=out2.change(im_fn,[put,fac_b,h],[out3,fac_b], show_progress=True)        
+        b5=out3.change(im_fn,[put,fac_b,h],[out4,fac_b], show_progress=True)
+        b6=out4.change(noth,None,message, show_progress=False)
+        swi=t_switch.change(clear,None,[t_switch,fac_b], cancels=[sta,b2,b3,b4,b5],show_progress=False)
+        #btn2.click(noth,None,message,cancels=[b1,sta,b2,b3,b4,b5,swi],show_progress=False)
+        btn2.click(clear_all, None,[fac_b,put,out1,out2,out3,out4,t_state,t_switch,message],cancels=[b1,sta,b2,b3,b4,b5,swi],show_progress=False)
+    b.queue(concurrency_count=100).launch(show_api=False)
+ac()