Update app.py

app.py

@@ -1,3 +1,144 @@
+import os
 import gradio as gr
+import torch
 
-gr.Interface.load("models/TheBloke/WizardLM-Uncensored-Falcon-7B-GPTQ").launch()
+from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
+
+model = AutoModelForCausalLM.from_pretrained("mrm8488/falcoder-7b", trust_remote_code=True)
+
+description = """# <h1 style="text-align: center; color: white;"><span style='color: #F26207;'> Code Completion with falcoder-7b </h1>
+<span style="color: white; text-align: center;"> falcoder-7b  You can click the button to generate your code.</span>"""
+
+
+token = os.environ["HUB_TOKEN"]
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+PAD_TOKEN = "<|pad|>"
+EOS_TOKEN = "<|endoftext|>"
+UNK_TOKEN = "<|unk|>"
+MAX_INPUT_TOKENS = 1024 # max tokens from context
+
+
+tokenizer = AutoTokenizer.from_pretrained(REPO, use_auth_token=token, trust_remote_code=True)
+tokenizer.truncation_side = "left" # ensures if truncate, then keep the last N tokens of the prompt going L -> R
+
+if device == "cuda":
+    model = AutoModelForCausalLM.from_pretrained(REPO, use_auth_token=token, trust_remote_code=True, low_cpu_mem_usage=True).to(device, dtype=torch.bfloat16)
+else:
+    model = AutoModelForCausalLM.from_pretrained(REPO, use_auth_token=token, trust_remote_code=True, low_cpu_mem_usage=True)
+
+model.eval()
+
+
+custom_css = """
+.gradio-container {
+    background-color: #0D1525; 
+    color:white
+}
+#orange-button {
+    background: #F26207 !important;
+    color: white;
+}
+.cm-gutters{
+    border: none !important;
+}
+"""
+
+def post_processing(prompt, completion):
+    return prompt + completion
+    # completion = "<span style='color: #499cd5;'>" + completion + "</span>"
+    # prompt = "<span style='color: black;'>" + prompt + "</span>"
+    # code_html = f"<hr><br><pre style='font-size: 14px'><code>{prompt}{completion}</code></pre><br><hr>"
+    # return code_html
+
+    
+def code_generation(prompt, max_new_tokens, temperature=0.2, seed=42, top_p=0.9, top_k=None, use_cache=True, repetition_penalty=1.0):
+
+    # truncates the prompt to MAX_INPUT_TOKENS if its too long
+    x = tokenizer.encode(prompt, return_tensors="pt", max_length=MAX_INPUT_TOKENS, truncation=True).to(device)
+    print("Prompt shape: ", x.shape) # just adding to see in the space logs in prod
+    set_seed(seed)
+    y = model.generate(x, 
+                       max_new_tokens=max_new_tokens, 
+                       temperature=temperature, 
+                       pad_token_id=tokenizer.pad_token_id, 
+                       eos_token_id=tokenizer.eos_token_id, 
+                       top_p=top_p,
+                       top_k=top_k,
+                       use_cache=use_cache,
+                       repetition_penalty=repetition_penalty
+                    )
+    completion = tokenizer.decode(y[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+    completion = completion[len(prompt):]
+    return post_processing(prompt, completion)
+
+
+demo = gr.Blocks(
+    css=custom_css
+)
+
+with demo:
+    gr.Markdown(value=description)
+    with gr.Row():
+        input_col , settings_col  = gr.Column(scale=6), gr.Column(scale=6), 
+        with input_col:
+            code = gr.Code(lines=28,label='Input', value="def sieve_eratosthenes(n):")
+        with settings_col:
+            with gr.Accordion("Generation Settings", open=True):
+                max_new_tokens= gr.Slider(
+                    minimum=8,
+                    maximum=128,
+                    step=1,
+                    value=48,
+                    label="Max Tokens",
+                )
+                temperature = gr.Slider(
+                    minimum=0.1,
+                    maximum=2.5,
+                    step=0.1,
+                    value=0.2,
+                    label="Temperature",
+                )
+                repetition_penalty = gr.Slider(
+                    minimum=1.0,
+                    maximum=1.9,
+                    step=0.1,
+                    value=1.0,
+                    label="Repetition Penalty. 1.0 means no penalty.",
+                )
+                seed = gr.Slider(
+                    minimum=0,
+                    maximum=1000,
+                    step=1,
+                    label="Random Seed"
+                )
+                top_p = gr.Slider(
+                    minimum=0.1,
+                    maximum=1.0,
+                    step=0.1,
+                    value=0.9,
+                    label="Top P",
+                )
+                top_k = gr.Slider(
+                    minimum=1,
+                    maximum=64,
+                    step=1,
+                    value=4,
+                    label="Top K",
+                )
+                use_cache = gr.Checkbox(
+                    label="Use Cache",
+                    value=True
+                )
+    
+    with gr.Row():
+        run = gr.Button(elem_id="orange-button", value="Generate")
+
+    # with gr.Row():
+    #     # _, middle_col_row_2, _ = gr.Column(scale=1), gr.Column(scale=6), gr.Column(scale=1)
+    #     # with middle_col_row_2:
+    #     output = gr.HTML(label="Generated Code")
+
+    event = run.click(code_generation, [code, max_new_tokens, temperature, seed, top_p, top_k, use_cache, repetition_penalty], code, api_name="predict")
+
+demo.queue(max_size=40).launch()