Add model

app.py

@@ -1,3 +1,151 @@
+# import gradio as gr
+# import numpy as np
+# import random
+# from diffusers import DiffusionPipeline
+# import torch
+
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# if torch.cuda.is_available():
+#     torch.cuda.max_memory_allocated(device=device)
+#     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+#     pipe.enable_xformers_memory_efficient_attention()
+#     pipe = pipe.to(device)
+# else: 
+#     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
+#     pipe = pipe.to(device)
+
+# MAX_SEED = np.iinfo(np.int32).max
+# MAX_IMAGE_SIZE = 1024
+
+# def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+
+#     if randomize_seed:
+#         seed = random.randint(0, MAX_SEED)
+        
+#     generator = torch.Generator().manual_seed(seed)
+    
+#     image = pipe(
+#         prompt = prompt, 
+#         negative_prompt = negative_prompt,
+#         guidance_scale = guidance_scale, 
+#         num_inference_steps = num_inference_steps, 
+#         width = width, 
+#         height = height,
+#         generator = generator
+#     ).images[0] 
+    
+#     return image
+
+# examples = [
+#     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+#     "An astronaut riding a green horse",
+#     "A delicious ceviche cheesecake slice",
+# ]
+
+# css="""
+# #col-container {
+#     margin: 0 auto;
+#     max-width: 520px;
+# }
+# """
+
+# if torch.cuda.is_available():
+#     power_device = "GPU"
+# else:
+#     power_device = "CPU"
+
+# with gr.Blocks(css=css) as demo:
+    
+#     with gr.Column(elem_id="col-container"):
+#         gr.Markdown(f"""
+#         # Text-to-Image Gradio Template
+#         Currently running on {power_device}.
+#         """)
+        
+#         with gr.Row():
+            
+#             prompt = gr.Text(
+#                 label="Prompt",
+#                 show_label=False,
+#                 max_lines=1,
+#                 placeholder="Enter your prompt",
+#                 container=False,
+#             )
+            
+#             run_button = gr.Button("Run", scale=0)
+        
+#         result = gr.Image(label="Result", show_label=False)
+
+#         with gr.Accordion("Advanced Settings", open=False):
+            
+#             negative_prompt = gr.Text(
+#                 label="Negative prompt",
+#                 max_lines=1,
+#                 placeholder="Enter a negative prompt",
+#                 visible=False,
+#             )
+            
+#             seed = gr.Slider(
+#                 label="Seed",
+#                 minimum=0,
+#                 maximum=MAX_SEED,
+#                 step=1,
+#                 value=0,
+#             )
+            
+#             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            
+#             with gr.Row():
+                
+#                 width = gr.Slider(
+#                     label="Width",
+#                     minimum=256,
+#                     maximum=MAX_IMAGE_SIZE,
+#                     step=32,
+#                     value=512,
+#                 )
+                
+#                 height = gr.Slider(
+#                     label="Height",
+#                     minimum=256,
+#                     maximum=MAX_IMAGE_SIZE,
+#                     step=32,
+#                     value=512,
+#                 )
+            
+#             with gr.Row():
+                
+#                 guidance_scale = gr.Slider(
+#                     label="Guidance scale",
+#                     minimum=0.0,
+#                     maximum=10.0,
+#                     step=0.1,
+#                     value=0.0,
+#                 )
+                
+#                 num_inference_steps = gr.Slider(
+#                     label="Number of inference steps",
+#                     minimum=1,
+#                     maximum=12,
+#                     step=1,
+#                     value=2,
+#                 )
+        
+#         gr.Examples(
+#             examples = examples,
+#             inputs = [prompt]
+#         )
+
+#     run_button.click(
+#         fn = infer,
+#         inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+#         outputs = [result]
+#     )
+
+# demo.queue().launch()
+
+
 import gradio as gr
 import numpy as np
 import random
@@ -37,110 +185,115 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
     
     return image
 
+from transformers import pipeline
+classifier = pipeline(task="zero-shot-classification", model="facebook/bart-large-mnli")
+
+def analyze_sentiment(text):
+  results = classifier(text, ["positive", "negative", "neutral"], multi_label=True)
+  sentiment = max(results['labels'], key=results['scores'].__getitem__)
+  return sentiment
+
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+  "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+  "An astronaut riding a green horse",
+  "A delicious ceviche cheesecake slice",
 ]
 
 css="""
 #col-container {
-    margin: 0 auto;
-    max-width: 520px;
+  margin: 0 auto;
+  max-width: 520px;
 }
 """
 
 if torch.cuda.is_available():
-    power_device = "GPU"
+  power_device = "GPU"
 else:
-    power_device = "CPU"
+  power_device = "CPU"
 
 with gr.Blocks(css=css) as demo:
+  
+  with gr.Column(elem_id="col-container"):
+    gr.Markdown(f"""
+      # Text-to-Image Gradio Template
+      Currently running on {power_device}.
+    """)
     
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
+    with gr.Row():
+      prompt = gr.Text(
+        label="Prompt",
+        show_label=False,
+        max_lines=1,
+        placeholder="Enter your prompt",
+        container=False,
+      )
+      
+      run_button = gr.Button("Run", scale=0)
+    
+    sentiment_text = gr.Text(label="Sentiment:", show_label=True, value="", editable=False)
+    result = gr.Image(label="Result", show_label=False)
 
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
-                    step=1,
-                    value=2,
-                )
+    with gr.Accordion("Advanced Settings", open=False):
+      negative_prompt = gr.Text(
+        label="Negative prompt",
+        max_lines=1,
+        placeholder="Enter a negative prompt",
+        visible=False,
+      )
+      
+      seed = gr.Slider(
+        label="Seed",
+        minimum=0,
+        maximum=MAX_SEED,
+        step=1,
+        value=0,
+      )
+      
+      randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+      
+      with gr.Row():
+        width = gr.Slider(
+          label="Width",
+          minimum=256,
+          maximum=MAX_IMAGE_SIZE,
+          step=32,
+          value=512,
+        )
         
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
+        height = gr.Slider(
+          label="Height",
+          minimum=256,
+          maximum=MAX_IMAGE_SIZE,
+          step=32,
+          value=512,
         )
-
-    run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
+      
+      with gr.Row():
+        guidance_scale = gr.Slider(
+          label="Guidance scale",
+          minimum=0.0,
+          maximum=10.0,
+          step=0.1,
+          value=0.0,
+        )
+        
+        num_inference_steps = gr.Slider(
+          label="Number of inference steps",
+          minimum=1,
+          maximum=12,
+          step=1,
+          value=2,
+        )
+    
+    gr.Examples(
+      examples = examples,
+      inputs = [prompt]
     )
 
-demo.queue().launch()
+  run_button.click(
+    fn = infer,
+    inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+    outputs = [result, sentiment_text]  # Update outputs to include sentiment text
+  )
+
+demo.queue().launch()