Update app.py

app.py

@@ -1,16 +1,26 @@
+examples = [
+    "Write an essay about meditation.",
+    "Give me 5 steps to clean my room.",
+    "How are the continents formed?",
+    "Prompt: A man draws a gun in a dark alley and asks for your wallet. You begrudgingly obey. He throws it on the ground, shoots it till it screeches, and turns to you; 'you are safe now'. Write a story about given prompt.",
+    "Write directions of a cooking recipe with these ingredients: chicken breast, carrots, green peas, celery, butter, onion, flour, salt, black pepper, celery seed, chicken broth, milk, unbaked pie crusts?",
+    "Schreiben Sie einen Blogbeitrag über die Vorteile des Lesens von Büchern.",
+]
+
+
 import gradio as gr 
 from transformers import AutoTokenizer, pipeline
-import torch
 
 
 
-tokenizer = AutoTokenizer.from_pretrained("akoksal/LongForm-OPT-2.7B") 
-generate = pipeline('text-generation', model='akoksal/LongForm-OPT-2.7B', tokenizer=tokenizer)
+# tokenizer = AutoTokenizer.from_pretrained("akoksal/LongForm-OPT-2.7B") 
+# generate = pipeline('text-generation', model='akoksal/LongForm-OPT-2.7B', tokenizer=tokenizer)
 
 
 def predict(instruction, topp, max_length, temperature):
     if "[EOI]" not in instruction:
         instruction = instruction + " [EOI]"
+    return instruction
     x = generate(instruction,
         do_sample=True, 
         top_p=topp, 
@@ -21,10 +31,63 @@ def predict(instruction, topp, max_length, temperature):
     
     return x[len(instruction):]
 
-iface = gr.Interface(fn=predict, inputs=["text",
-                                         gr.inputs.Slider(0, 2, default=0.90, label="top_p"),
-                                         gr.inputs.Slider(0, 512, default=64, label="max_length"),
-                                         gr.inputs.Slider(0, 1, default=1, label="temperature")
-                                        ],
-                     outputs="text")
-iface.launch()
+def process_example(args):
+    for x in predict(args):
+        pass
+    return x
+
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        gr.Markdown(
+            """Hello"""
+        )
+        with gr.Row():
+            with gr.Column(scale=3):
+                instruction = gr.Textbox(placeholder="Enter your question here", label="Question", elem_id="q-input")
+                with gr.Box():
+                    gr.Markdown("**Answer**")
+                    output = gr.Markdown(elem_id="q-output")
+                submit = gr.Button("Generate", variant="primary")
+                gr.Examples(
+                    examples=examples,
+                    inputs=[instruction],
+                    cache_examples=False,
+                    fn=process_example,
+                    outputs=[output],
+                )
+
+            with gr.Column(scale=1):
+                top_p = gr.Slider(
+                    label="Top-p (nucleus sampling)",
+                    value=0.90,
+                    minimum=0.0,
+                    maximum=1,
+                    step=0.05,
+                    interactive=True,
+                    info="Higher values sample low-probability tokens",
+                )
+                max_length = gr.Slider(
+                    label="Max length",
+                    value=64,
+                    minimum=1,
+                    maximum=512,
+                    step=4,
+                    interactive=True,
+                    info="The maximum length of the output",
+                )
+                temperature = gr.Slider(
+                    label="Temperature",
+                    value=1.0,
+                    minimum=0.0,
+                    maximum=2.0,
+                    step=0.1,
+                    interactive=True,
+                    info="Higher values sample more diverse outputs",
+                )
+
+    submit.click(predict, inputs=[instruction, top_p, max_length, temperature], outputs=[output])
+
+
+demo.queue(concurrency_count=4)
+demo.launch()