Create app.py

app.py

@@ -0,0 +1,495 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, STOKEStreamer
+from threading import Thread
+import json
+import torch
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import to_hex
+from bs4 import BeautifulSoup
+
+def clean_html(html_content):
+    # Parse the HTML
+    soup = BeautifulSoup(html_content, 'html.parser')
+
+    # Remove all elements with class 'small-text'
+    for element in soup.find_all(class_='small-text'):
+        element.decompose()  # Removes the element from the tree
+
+    # Get the plain text, stripping any remaining HTML tags
+    cleaned_text = soup.get_text()
+
+    return cleaned_text.strip().replace("  ", " ").replace("( ", "(").replace(" )", ")")
+
+# Reusing the original MLP class and other functions (unchanged) except those specific to Streamlit
+class MLP(torch.nn.Module):
+    def __init__(self, input_dim, output_dim, hidden_dim=1024, layer_id=0, cuda=False):
+        super(MLP, self).__init__()
+        self.fc1 = torch.nn.Linear(input_dim, hidden_dim)
+        self.fc3 = torch.nn.Linear(hidden_dim, output_dim)
+        self.layer_id = layer_id
+        if cuda:
+            self.device = "cuda"
+        else:
+            self.device = "cpu"
+        self.to(self.device)
+
+    def forward(self, x):
+        x = torch.flatten(x, start_dim=1)
+        x = torch.relu(self.fc1(x))
+        x = self.fc3(x)
+        return torch.argmax(x, dim=-1).cpu().detach(), torch.softmax(x, dim=-1).cpu().detach()
+
+def map_value_to_color(value, colormap_name='tab20c'):
+    value = np.clip(value, 0.0, 1.0)
+    colormap = plt.get_cmap(colormap_name)
+    rgba_color = colormap(value)
+    css_color = to_hex(rgba_color)
+    return css_color + "88"
+
+# Caching functions for model and classifier
+model_cache = {}
+
+def get_model_and_tokenizer(name):
+    if name not in model_cache:
+        tok = AutoTokenizer.from_pretrained(name, token=os.getenv('HF_TOKEN'))
+        model = AutoModelForCausalLM.from_pretrained(name, token=os.getenv('HF_TOKEN'), torch_dtype="bfloat16")
+        #model = AutoModelForCausalLM.from_pretrained(name, token=, load_in_4bit=True)
+        model_cache[name] = (model, tok)
+    return model_cache[name]
+
+def get_classifiers_for_model(att_size, emb_size, device, config_paths):
+    config = {
+        "classifier_token": json.load(open(os.path.join(config_paths["classifier_token"], "config.json"), "r")),
+        "classifier_span": json.load(open(os.path.join(config_paths["classifier_span"], "config.json"), "r"))
+    }
+    layer_id = config["classifier_token"]["layer"]
+    
+    classifier_span = MLP(att_size, 2, hidden_dim=config["classifier_span"]["classifier_dim"]).to(device)
+    classifier_span.load_state_dict(torch.load(os.path.join(config_paths["classifier_span"], "checkpoint.pt"), map_location=device))
+
+    classifier_token = MLP(emb_size, len(config["classifier_token"]["label_map"]), layer_id=layer_id, hidden_dim=config["classifier_token"]["classifier_dim"]).to(device)
+    classifier_token.load_state_dict(torch.load(os.path.join(config_paths["classifier_token"], "checkpoint.pt"), map_location=device))
+
+    return classifier_span, classifier_token, config["classifier_token"]["label_map"]
+
+def find_datasets_and_model_ids(root_dir):
+    datasets = {}
+    for root, dirs, files in os.walk(root_dir):
+        if 'config.json' in files and 'stoke_config.json' in files:
+            config_path = os.path.join(root, 'config.json')
+            stoke_config_path = os.path.join(root, 'stoke_config.json')
+
+            with open(config_path, 'r') as f:
+                config_data = json.load(f)
+                model_id = config_data.get('model_id')
+                if model_id:
+                    dataset_name = os.path.basename(os.path.dirname(config_path))
+
+            with open(stoke_config_path, 'r') as f:
+                stoke_config_data = json.load(f)
+                if model_id:
+                    dataset_name = os.path.basename(os.path.dirname(stoke_config_path))
+                    datasets.setdefault(model_id, {})[dataset_name] = stoke_config_data
+    return datasets
+
+def filter_spans(spans_and_values):
+    if spans_and_values == []:
+        return [], []
+    # Create a dictionary to store spans based on their second index values
+    span_dict = {}
+
+    spans, values = [x[0] for x in spans_and_values], [x[1] for x in spans_and_values]
+
+    # Iterate through the spans and update the dictionary with the highest value
+    for span, value in zip(spans, values):
+        start, end = span
+        if start > end or end - start > 15 or start == 0:
+            continue
+        current_value = span_dict.get(end, None)
+
+        if current_value is None or current_value[1] < value:
+            span_dict[end] = (span, value)
+
+    if span_dict == {}:
+        return [], []
+    # Extract the filtered spans and values
+    filtered_spans, filtered_values = zip(*span_dict.values())
+
+    return list(filtered_spans), list(filtered_values)
+
+def remove_overlapping_spans(spans):
+    # Sort the spans based on their end points
+    sorted_spans = sorted(spans, key=lambda x: x[0][1])
+    
+    non_overlapping_spans = []
+    last_end = float('-inf')
+    
+    # Iterate through the sorted spans
+    for span in sorted_spans:
+        start, end = span[0]
+        value = span[1]
+        
+        # If the current span does not overlap with the previous one
+        if start >= last_end:
+            non_overlapping_spans.append(span)
+            last_end = end
+        else:
+            # If it overlaps, choose the one with the highest value
+            existing_span_index = -1
+            for i, existing_span in enumerate(non_overlapping_spans):
+                if existing_span[0][1] <= start:
+                    existing_span_index = i
+                    break
+            if existing_span_index != -1 and non_overlapping_spans[existing_span_index][1] < value:
+                non_overlapping_spans[existing_span_index] = span
+    
+    return non_overlapping_spans
+
+def generate_html_no_overlap(tokenized_text, spans):
+    current_index = 0
+    html_content = ""
+
+    for (span_start, span_end), value in spans:
+        # Add text before the span
+        html_content += "".join(tokenized_text[current_index:span_start])
+
+        # Add the span with underlining
+        html_content += "<b><u>"
+        html_content += "".join(tokenized_text[span_start:span_end])
+        html_content += "</u></b> "
+
+        current_index = span_end
+
+    # Add any remaining text after the last span
+    html_content += "".join(tokenized_text[current_index:])
+
+    return html_content
+
+
+def generate_html_spanwise(token_strings, tokenwise_preds, spans, tokenizer, new_tags):
+
+    # spanwise annotated text
+    annotated = []
+    span_ends = -1
+    in_span = False
+
+    out_of_span_tokens = []
+    for i in reversed(range(len(tokenwise_preds))):
+
+        if in_span:
+            if i >= span_ends:
+                continue
+            else:
+                in_span = False
+
+        predicted_class = ""
+        style = ""
+
+        span = None
+        for s in spans:
+            if s[1] == i+1:
+                span = s
+
+        if tokenwise_preds[i] != 0 and span is not None:
+            predicted_class = f"highlight spanhighlight"
+            style = f"background-color: {map_value_to_color((tokenwise_preds[i]-1)/(len(new_tags)-1))}"
+            if tokenizer.convert_tokens_to_string([token_strings[i]]).startswith(" "):
+                annotated.append("Ġ")
+
+            span_opener = f"Ġ<span class='{predicted_class}' data-tooltip-text='{new_tags[tokenwise_preds[i]]}' style='{style}'>".replace(" ", "Ġ")
+            span_end = f"<span class='small-text'>{new_tags[tokenwise_preds[i]]}</span></span>"
+            annotated.extend(out_of_span_tokens)
+            out_of_span_tokens = []
+            span_ends = span[0]
+            in_span = True
+            annotated.append(span_end)
+            annotated.extend([token_strings[x] for x in reversed(range(span[0], span[1]))])
+            annotated.append(span_opener)
+        else:
+            out_of_span_tokens.append(token_strings[i])
+
+    annotated.extend(out_of_span_tokens)
+
+    return [x for x in reversed(annotated)]
+
+def gen_json(input_text, max_new_tokens):
+    streamer = STOKEStreamer(tok, classifier_token, classifier_span)
+
+    new_tags = label_map
+    
+    inputs = tok([f"  {input_text}"], return_tensors="pt").to(model.device)
+    generation_kwargs = dict(
+        inputs, streamer=streamer, max_new_tokens=max_new_tokens, 
+        repetition_penalty=1.2, do_sample=False
+    )
+
+    def generate_async():
+        model.generate(**generation_kwargs)
+
+    thread = Thread(target=generate_async)
+    thread.start()
+
+    # Display generated text as it becomes available
+    output_text = ""
+    text_tokenwise = ""
+    text_spans = ""
+    removed_spans = ""
+    tags = []
+    spans = []
+    for new_text in streamer:
+        if new_text[1] is not None and new_text[2] != ['']:
+            text_tokenwise = ""
+            output_text = ""
+            tags.extend(new_text[1])
+            spans.extend(new_text[-1])
+
+            # Tokenwise Classification
+            for tk, pred in zip(new_text[2],tags):
+                if pred != 0:
+                    style = f"background-color: {map_value_to_color((pred-1)/(len(new_tags)-1))}"
+                    if tk.startswith(" "):
+                        text_tokenwise += " "
+                    text_tokenwise += f"<span class='tooltip highlight' data-tooltip-text='{new_tags[pred]}' style='{style}'>{tk}</span>"
+                    output_text += tk
+                else:
+                    text_tokenwise += tk
+                    output_text += tk
+
+            # Span Classification
+            text_spans = ""
+            if len(spans) > 0:
+                filtered_spans = remove_overlapping_spans(spans)
+                text_spans = generate_html_no_overlap(new_text[2], filtered_spans)
+                if len(spans) - len(filtered_spans) > 0:
+                    removed_spans = f"{len(spans) - len(filtered_spans)} span(s) hidden due to overlap."
+            else:
+                for tk in new_text[2]:
+                    text_spans += f"{tk}"
+
+            # Spanwise Classification
+            annotated_tokens = generate_html_spanwise(new_text[2], tags, [x for x in filter_spans(spans)[0]], tok, new_tags)
+            generated_text_spanwise = tok.convert_tokens_to_string(annotated_tokens).replace("<|endoftext|>", "").replace("<|begin_of_text|>", "")
+
+            output = f"{css}<br>"
+            output += generated_text_spanwise.replace("\n", " ").replace("$", "$") + "\n<br>"
+            #output += "<h5>Show tokenwise classification</h5>\n" + text_tokenwise.replace("\n", " ").replace("$", "\\$").replace("<|endoftext|>", "").replace("<|begin_of_text|>", "")
+            #output += "</details><details><summary>Show spans</summary>\n" + text_spans.replace("\n", " ").replace("$", "\\$")
+            #if removed_spans != "":
+            #    output += f"<br><br><i>({removed_spans})</i>"
+            list_of_spans = [{"name": tok.convert_tokens_to_string(new_text[2][x[0]:x[1]]).strip(), "type": new_tags[tags[x[1]-1]]} for x in filter_spans(spans)[0] if new_tags[tags[x[1]-1]] != "O"]
+
+            out_dict = {"text": output_text.replace("<|endoftext|>", "").replace("<|begin_of_text|>", "".strip()), "entites": list_of_spans}
+            
+            yield out_dict
+    return
+
+# Creating the Gradio Interface
+def generate_text(input_text, messages=None):
+    if input_text == "":
+        yield "Please enter some text first."
+        return
+    
+    token_limit=250
+    #print([clean_html(x["content"]) for x in messages])
+    
+    streamer = STOKEStreamer(tok, classifier_token, classifier_span)
+
+    new_tags = label_map
+    
+    if messages is None:
+        messages = []
+    else:
+        messages = []
+    system="""You are a knowledge assistant. Keep your responses very short."""
+    messages = [{"role": "system", "content": system}]+ [{"role": x["role"], "content": clean_html(x["content"])} for x in messages] +[{"role": "user", "content": input_text}]
+    input_text = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+    inputs = tok([input_text], return_tensors="pt").to(model.device)
+
+    if len(inputs.input_ids[0]) > 80:
+        yield [{"role": "assistant", "content": "Your message is too long for this demo, sorry :("}]
+        return
+
+    #inputs = tok([f"  {input_text[:200]}"], return_tensors="pt").to(model.device)
+    #inputs = tok([input_text[:200]], return_tensors="pt").to(model.device)
+    generation_kwargs = dict(
+        inputs, streamer=streamer, max_new_tokens=token_limit-len(inputs.input_ids[0]), 
+        repetition_penalty=1.2, do_sample=False
+    )
+
+    def generate_async():
+        model.generate(**generation_kwargs)
+
+    thread = Thread(target=generate_async)
+    thread.start()
+
+    # Display generated text as it becomes available
+    output_text = ""
+    text_tokenwise = ""
+    text_spans = ""
+    removed_spans = ""
+    tags = []
+    spans = []
+    for new_text in streamer:
+        if new_text[1] is not None and new_text[2] != ['']:
+            text_tokenwise = ""
+            output_text = ""
+            tags.extend(new_text[1])
+            spans.extend(new_text[-1])
+
+            # Tokenwise Classification
+            for tk, pred in zip(new_text[2],tags):
+                if pred != 0:
+                    style = f"background-color: {map_value_to_color((pred-1)/(len(new_tags)-1))}"
+                    if tk.startswith(" "):
+                        text_tokenwise += " "
+                    text_tokenwise += f"<span class='tooltip highlight' data-tooltip-text='{new_tags[pred]}' style='{style}'>{tk}</span>"
+                    output_text += tk
+                else:
+                    text_tokenwise += tk
+                    output_text += tk
+
+            # Span Classification
+            text_spans = ""
+            if len(spans) > 0:
+                filtered_spans = remove_overlapping_spans(spans)
+                text_spans = generate_html_no_overlap(new_text[2], filtered_spans)
+                if len(spans) - len(filtered_spans) > 0:
+                    removed_spans = f"{len(spans) - len(filtered_spans)} span(s) hidden due to overlap."
+            else:
+                for tk in new_text[2]:
+                    text_spans += f"{tk}"
+
+            # Spanwise Classification
+            annotated_tokens = generate_html_spanwise(new_text[2], tags, [x for x in filter_spans(spans)[0]], tok, new_tags)
+            generated_text_spanwise = tok.convert_tokens_to_string(annotated_tokens).replace("<|endoftext|>", "").replace("<|begin_of_text|>", "")
+
+            output = generated_text_spanwise
+            #output += "<h5>Show tokenwise classification</h5>\n" + text_tokenwise.replace("\n", " ").replace("$", "\\$").replace("<|endoftext|>", "").replace("<|begin_of_text|>", "")
+            #output += "</details><details><summary>Show spans</summary>\n" + text_spans.replace("\n", " ").replace("$", "\\$")
+            #if removed_spans != "":
+            #    output += f"<br><br><i>({removed_spans})</i>"
+            list_of_spans = [{"name": tok.convert_tokens_to_string(new_text[2][x[0]:x[1]]).strip(), "type": new_tags[tags[x[1]-1]]} for x in filter_spans(spans)[0] if new_tags[tags[x[1]-1]] != "O"]
+
+            out_dict = {"text": output_text.replace("<|endoftext|>", "").replace("<|begin_of_text|>", "").strip(), "entites": list_of_spans}
+            
+            html_out = output.replace("<|endoftext|>", "").replace("<|begin_of_text|>", "").strip().split("<|end_header_id|>\n\n")[-1].replace("**", "")
+            
+            yield [messages[-1]] + [{"role": "assistant", "content": html_out}]
+    return
+
+# Load datasets and models for the Gradio app
+datasets = find_datasets_and_model_ids("data/")
+available_models = list(datasets.keys())
+available_datasets = {model: list(datasets[model].keys()) for model in available_models}
+available_configs = {model: {dataset: list(datasets[model][dataset].keys()) for dataset in available_datasets[model]} for model in available_models}
+
+def update_datasets(model_name):
+    return available_datasets[model_name]
+
+def update_configs(model_name, dataset_name):
+    return available_configs[model_name][dataset_name]
+
+model_id = "meta-llama/Llama-3.2-1B-Instruct"
+data_id = "STOKE_500_wikiqa"
+config_id = "default"
+
+#model_id = "gpt2"
+#data_id = "1_NER"
+#config_id = "default"
+
+model, tok = get_model_and_tokenizer(model_id)
+if torch.cuda.is_available():
+    model.cuda()
+
+# Load model classifiers
+try:
+    classifier_span, classifier_token, label_map = get_classifiers_for_model(
+        model.config.n_head * model.config.n_layer, model.config.n_embd, model.device,
+        datasets[model_id][data_id][config_id]
+    )
+except:
+    classifier_span, classifier_token, label_map = get_classifiers_for_model(
+        model.config.num_attention_heads * model.config.num_hidden_layers, model.config.hidden_size, model.device,
+        datasets[model_id][data_id][config_id]
+    )
+
+
+css = """
+    <style>
+    .prose {
+        line-height: 200%;
+    }
+    .highlight {
+        display: inline;
+    }
+    .highlight::after {
+        background-color: var(data-color);
+    }
+    .spanhighlight {
+        padding: 2px 5px;
+        border-radius: 5px;
+    }
+    .tooltip {
+    position: relative;
+    display: inline-block;
+}
+
+.tooltip::after {
+    content: attr(data-tooltip-text); /* Set content from data-tooltip-text attribute */
+    display: none;
+    position: absolute;
+    background-color: #333;
+    color: #fff;
+    padding: 5px;
+    border-radius: 5px;
+    bottom: 100%; /* Position it above the element */
+    left: 50%;
+    transform: translateX(-50%);
+    width: auto;
+    min-width: 120px;
+    margin: 0 auto;
+    text-align: center;
+}
+
+.tooltip:hover::after {
+    display: block; /* Show the tooltip on hover */
+}
+
+.small-text {
+    padding: 2px 5px;
+    background-color: white;
+    border-radius: 5px;
+    font-size: xx-small;
+    margin-left: 0.5em;
+    vertical-align: 0.2em;
+    font-weight: bold;
+    color: grey!important;
+}
+footer {
+    display:none !important
+    } 
+    .gradio-container {
+        padding: 0!important;
+        height:400px;
+        }
+    </style>"""
+"""
+with gr.Blocks(css=css, elem_id="chatbox") as demo:
+    gr.ChatInterface(generate_text, examples=["Who where the Beatles?", "Whats the GDP of Norway?", "List some fun things to do in Miami", "What do you know about the KIT in Karlsruhe?", "Give me a list of the most iconic 90s songs", "Whats the typical cost of a pizza in New York City?", "Got any suggestions for a day trip from Miami?", "Tell me about the climate in Europe.", "Where can I go scuba diving?", "give me a list of famous people and their years of birth"], type="messages")
+"""
+
+example_messages=[{'role': 'user', 'content': 'Who where the Beatles?'}, {'role': 'assistant', 'content': "The <span class='highlight spanhighlight' data-tooltip-text='ORG' style='background-color: #756bb188'> Beatles<span class='small-text'>ORG</span></span> were a  <span class='highlight spanhighlight' data-tooltip-text='NORP' style='background-color: #a1d99b88'> British<span class='small-text'>NORP</span></span> rock band formed in  <span class='highlight spanhighlight' data-tooltip-text='GPE' style='background-color: #e6550d88'> Liverpool<span class='small-text'>GPE</span></span>,  <span class='highlight spanhighlight' data-tooltip-text='GPE' style='background-color: #e6550d88'> England<span class='small-text'>GPE</span></span> in   <span class='highlight spanhighlight' data-tooltip-text='DATE' style='background-color: #6baed688'>1960<span class='small-text'>DATE</span></span> that rose to fame with their music and iconic style during the late  <span class='highlight spanhighlight' data-tooltip-text='DATE' style='background-color: #6baed688'>1950s<span class='small-text'>DATE</span></span> and <span class='highlight spanhighlight' data-tooltip-text='DATE' style='background-color: #6baed688'> early 1960s<span class='small-text'>DATE</span></span>. The group consisted of <span class='highlight spanhighlight' data-tooltip-text='PERSON' style='background-color: #bcbddc88'> John Lennon<span class='small-text'>PERSON</span></span> (  <span class='highlight spanhighlight' data-tooltip-text='PERSON' style='background-color: #bcbddc88'>Ringo Starr<span class='small-text'>PERSON</span></span>),  <span class='highlight spanhighlight' data-tooltip-text='PERSON' style='background-color: #bcbddc88'> Paul McCartney<span class='small-text'>PERSON</span></span>,  <span class='highlight spanhighlight' data-tooltip-text='PERSON' style='background-color: #bcbddc88'> George Harrison<span class='small-text'>PERSON</span></span>, and  <span class='highlight spanhighlight' data-tooltip-text='PERSON' style='background-color: #bcbddc88'> Ringo Starr<span class='small-text'>PERSON</span></span>. They're widely regarded as one of the most influential and successful bands in popular culture history."}]
+
+with gr.Blocks(css=css, fill_width=True) as demo:
+    chatbot = gr.Chatbot(type="messages", value=example_messages)
+    msg = gr.Textbox(submit_btn=True)
+    msg.submit(lambda: None, None, chatbot).then(generate_text, msg, chatbot, queue="queue")
+    # Add an examples section for users to pick from predefined messages
+    examples = gr.Examples(examples=["Who where the Beatles?", "Whats the GDP of Norway?", "List some fun things to do in Miami", "What do you know about the KIT in Karlsruhe?"], inputs=msg, run_on_click=True, fn=generate_text, outputs=chatbot)
+
+
+
+demo.launch(server_name="0.0.0.0", server_port=7861)
+