Update app.py

app.py

@@ -3,7 +3,9 @@ import os
 import requests
 import random
 import time
-
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+from PIL import Image
 from transformers import pipeline
 
 # Load the pipeline for text generation
@@ -32,111 +34,34 @@ iface = gr.Interface(
     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"
 )
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
+model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B")
 
-# Launch the interface
-iface.launch(show_api=True)
-
-name2 = "stabilityai/stable-diffusion-xl-base-1.0"
+def generate_image(text):
+    # Tokenize input text
+    input_ids = tokenizer.encode(text, return_tensors="pt")
 
-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"
+    # Generate image conditioned on input text
+    output = model.generate(input_ids, do_sample=True, max_length=128, num_return_sequences=1)
 
-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>""")
+    # Decode generated image tokens to get image
+    image_bytes = tokenizer.decode(output[0], skip_special_tokens=True)
 
-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)
+    # Convert image bytes to PIL image
+    image = Image.open(image_bytes)
 
-        with gr.Row():
-            out1 = gr.Image()
-            out2 = gr.Image()
-        with gr.Row():
-            out3 = gr.Image()
-            out4 = gr.Image()
+    return 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)
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=gr.inputs.Textbox(lines=3, label="Input Text"),
+    outputs="image",
+    title="Text-to-Image Generator",
+    description="Generate images from text using Hugging Face's GPT-Neo model.",
+    theme="huggingface"
+)
 
-        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()
+# Launch the interface
+iface.launch(show_api=True)