Create app.py

app.py

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+# gradio app for the LLM model --> use the retr environment
+# Run the script and open the link in the browser.
+
+import os
+import json
+import pandas as pd
+import datasets
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# training from scratch with latbert tokenizer
+CHECKPOINT_PATH= 'scratch_2-nodes_tokenizer_latbert-original_packing_fcocchi/'
+CHECKPOINT_PATH= 'itserr/latin_llm_alpha'
+
+print(f"Loading model from: {CHECKPOINT_PATH}")
+tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_PATH, token=os.environ['HF_TOKEN_READ'])
+model = AutoModelForCausalLM.from_pretrained(CHECKPOINT_PATH, token=os.environ['HF_TOKEN_READ'])
+
+preference_dataset_name= "itserr/latin_gpt_preferences"
+global dataset_hf
+dataset_hf = datasets.load_dataset(preference_dataset_name, token=os.environ['HF_TOKEN_READ'], download_mode='force_redownload')
+dataset_hf = dataset_hf['train'].to_pandas()
+print(dataset_hf.shape)
+
+description="""
+This is a Latin Language Model (LLM) based on GPT-2 and it was trained on a large corpus of Latin texts and can generate text in Latin. \n
+Demo instructions:
+- Enter a prompt in Latin in the Input Text box.
+- Select the temperature value to control the randomness of the generated text (higher value produce a more creative and unstable answer).
+- Click the 'Generate Text' button to trigger model generation.
+- (Optional) insert a Feedback text in the box.
+- Click the 'Like' or 'Dislike' button to judge the generation correctness. 
+"""
+title= "(L<sup>2</sup>) - Latin Language Model"
+article= "hello world ..."
+examples= ['Accidere ex una scintilla', 'Audacter calumniare,', 'Consolatium misero comites']
+logo_image= '/work/pnrr_itserr/latin_model/demo_gpt/ITSERR_row_logo.png'
+
+def generate_text(prompt, slider):
+    if torch.cuda.is_available(): device = torch.device("cuda")      
+    else: 
+        device = torch.device("cpu")
+        print("No GPU available")
+    
+    print("***** Generate *****")
+    text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device)
+    #generated_text = text_generator(prompt, max_length=100)
+    generated_text = text_generator(prompt, max_length=50, do_sample=True, temperature=slider, repetition_penalty=2.0, truncation=True)
+    return generated_text[0]['generated_text']
+
+# Function to handle user preferences
+def handle_preference(preference, input, output, feedback, temp_value):
+    """
+    Format values stored in preferences:
+        - input text
+        - output generated text
+        - user feedback
+        - float temperature value
+    """
+    # first time staring from a csv file (edited the present one), then work with parquet file
+    # input_text,generated_text,feedback,temperature,like,dislike,count_like,count_dislike
+    global dataset_hf
+    if input == output:
+        output_tuple= ("", "")
+    else:
+        output_tuple= (input, output.split(input)[-1])
+    if preference == "like":
+        dislike=0
+        like=1
+        count_like= dataset_hf.iloc[-1]['count_like']
+        count_dislike= dataset_hf.iloc[-1]['count_dislike']
+        if output_tuple[1] != "" :
+            count_like= dataset_hf.iloc[-1]['count_like'] + 1
+
+    elif preference == "dislike":
+        dislike=1
+        like=0
+        count_like= dataset_hf.iloc[-1]['count_like']
+        count_dislike= dataset_hf.iloc[-1]['count_dislike']
+        if output_tuple[1] != "" :
+            count_dislike= dataset_hf.iloc[-1]['count_dislike'] + 1
+
+    inp_text= output_tuple[0]
+    out_text= output_tuple[1]
+    new_data = pd.DataFrame({'input_text': inp_text, 'generated_text': out_text, 'feedback': feedback,
+                              'temperature': float(temp_value), 'like': like, 'dislike': dislike, 
+                              'count_like': count_like, 'count_dislike': count_dislike}, index=[0])
+    dataset_hf = pd.concat([dataset_hf, new_data], ignore_index=True)
+    hf_dataset = datasets.Dataset.from_pandas(dataset_hf)
+    dataset_dict = datasets.DatasetDict({"train": hf_dataset})
+    dataset_dict.push_to_hub(preference_dataset_name, token=os.environ['HF_TOKEN_WRITE'])
+    
+    # print dataset statistics
+    print(f"Admin log: like: {count_like} and dislike: {count_dislike}")
+    return f"You select '{preference}' as answer of the model generation. Thank you for your time!"
+
+custom_css = """
+#logo {
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+    width: 280px;
+    height: 140px;
+}
+"""
+
+with gr.Blocks(css=custom_css) as demo:
+    gr.Image(logo_image, elem_id="logo")
+    gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>")
+    gr.Markdown(description)
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(lines=5, placeholder="Enter latin text here...", label="Input Text")
+        with gr.Column():
+            output_text = gr.Textbox(lines=5, placeholder="Output text will appear here...", label="Output Text")
+
+    gr.Examples(examples=examples, inputs=input_text)
+    temperature_slider = gr.Slider(minimum=0.1, maximum=5.0, step=0.1, value=1.0, label="Temperature")
+    
+    clean_button = gr.Button("Generate Text")
+    clean_button.click(fn=generate_text, inputs=[input_text, temperature_slider], outputs=output_text)
+    feedback_output = gr.Textbox(lines=1, placeholder="If you want to provide a feedback, please fill this box ...", label="Feedback")
+
+    with gr.Row():
+        like_button = gr.Button("Like")
+        dislike_button = gr.Button("Dislike")
+
+    button_output = gr.Textbox(lines=1, placeholder="Please submit your choice", label="Latin Language Model Demo")
+    like_button.click(fn=lambda x,y,z,v: handle_preference("like", x, y, z, v), inputs=[input_text, output_text, feedback_output, temperature_slider], outputs=button_output)
+    dislike_button.click(fn=lambda x,y,z,v: handle_preference("dislike", x, y, z, v), inputs=[input_text, output_text, feedback_output, temperature_slider], outputs=button_output)
+    #gr.Markdown(article)
+
+demo.launch(share=True, debug=True)