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app.py

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer, STOKEStreamer
+from threading import Thread
+import json
+import torch
+import matplotlib.pyplot as plt
+from matplotlib.colors import to_hex
+import numpy as np
+import os
+import urllib.request
+import zipfile
+
+
+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)  # Input layer to hidden layer
+        self.fc3 = torch.nn.Linear(hidden_dim, output_dim)  # Hidden layer to output layer
+        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'):
+    """
+    Map a value between 0 and 1 to a CSS color using a Python colormap.
+
+    Args:
+        value (float): A value between 0 and 1.
+        colormap_name (str): The name of the colormap to use (e.g., 'viridis').
+
+    Returns:
+        str: A CSS color string in the form 'rgb(r, g, b)'.
+    """
+    # Ensure the value is within the range [0, 1]
+    value = np.clip(value, 0.0, 1.0)
+
+    # Get the colormap
+    colormap = plt.get_cmap(colormap_name)
+
+    # Map the value to a color
+    rgba_color = colormap(value)
+
+    # Convert the RGBA color to CSS format
+    css_color = to_hex(rgba_color)
+
+    return css_color + "88"
+
+@st.cache_resource
+def get_model_and_tokenizer(name):
+    # Load pre-trained model and tokenizer
+    tok = AutoTokenizer.from_pretrained(name)
+    model = AutoModelForCausalLM.from_pretrained(name)
+    return model, tok
+
+@st.cache_resource
+def get_classifiers_for_model(att_size, emb_size, device, config_paths):
+    classifier_token = None
+    #print(config)
+    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))
+
+    print(sum(p.numel() for p in classifier_span.parameters()), sum(p.numel() for p in classifier_token.parameters()))
+
+    return classifier_span, classifier_token, config["classifier_token"]["label_map"]
+
+def get_available_models():
+    available_models = []
+    for model_name in ["gpt2", "gpt2-xl"]:
+        if os.path.isfile(f"checkpoints/{model_name}/config.json"):
+            available_models.append(model_name)
+    return available_models
+
+def get_available_datasets(model_name):
+    available_datasets = []
+    config_path = f"checkpoints/{model_name}/config.json"
+    if os.path.isfile(config_path):
+        with open(config_path, "r") as f:
+            config = json.load(f)
+            # Assuming datasets are keys in config.json
+            available_datasets = list(config.keys())
+    return available_datasets
+
+def download_and_extract_zip(url, extract_dir):
+    # Determine the parent directory
+    parent_dir = os.path.split(os.path.dirname(extract_dir))[-2]
+    print(parent_dir)
+    
+    # Download the zip file to the parent directory
+    zip_file_path = os.path.join(parent_dir, "data.zip")
+    urllib.request.urlretrieve(url, zip_file_path)
+    
+    # Extract the zip file
+    with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+        zip_ref.extractall(parent_dir)
+    
+    # Remove the zip file
+    os.remove(zip_file_path)
+
+def find_datasets_and_model_ids(root_dir):
+    datasets = {}
+    
+    # Check if the root directory exists
+    if not os.path.exists(root_dir):
+        # If root directory doesn't exist, download a zip file and unpack it
+        print("Root directory doesn't exist. Downloading zip file...")
+        url = "https://drive.usercontent.google.com/download?id=1dHjH_J0zuPS-SDVrh49tMpIx5ramu_hc&export=download&authuser=0&confirm=t&uuid=4efcec77-571c-44c7-82f1-f39ddae50eb5&at=APZUnTW8g-Ab4PUT0-B9mh4jQSc-%3A1711040271924"  # Replace with your actual download URL
+        download_and_extract_zip(url, root_dir)
+        print("Zip file downloaded and unpacked successfully.")
+
+    
+    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
+
+
+# Main content
+st.title("Playground")
+
+# Sidebar for model and dataset selection
+with st.sidebar:
+    st.subheader("Model and Dataset Selection")
+    datasets = find_datasets_and_model_ids("data/")
+    available_models = datasets.keys()
+    print(datasets)
+    if available_models:
+        model_selection = st.selectbox("Select Model", available_models)
+    else:
+        st.error("No models available. Please check the file paths.")
+
+    # Select dataset based on selected model
+    available_datasets = datasets[model_selection]
+    if available_datasets:
+        dataset_selection = st.selectbox("Select Dataset", available_datasets)
+    else:
+        st.error("No datasets available for the selected model.")
+
+    # Select dataset based on selected model
+    available_configs = datasets[model_selection][dataset_selection]
+    if available_configs:
+        config_selection = st.selectbox("Select Config", available_configs.keys())
+    else:
+        st.error("No configs available for the selected dataset.")
+
+# Load model and streamer based on selections
+model, tok = get_model_and_tokenizer(model_selection)
+if torch.cuda.is_available():
+    model.cuda()
+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_selection][dataset_selection][config_selection])
+streamer = STOKEStreamer(tok, classifier_token, classifier_span)
+
+new_tags = label_map
+
+
+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
+
+
+css = """
+    <style>
+    .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;
+}
+    </style>"""
+
+
+def generate_html_spanwise(token_strings, tokenwise_preds, spans, tokenizer):
+
+    # 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)]
+
+# Define function to generate text based on input
+def generate_text(generation_kwargs, output_field):
+    
+    # Function to generate text in a separate thread
+    def generate_async():
+        model.generate(**generation_kwargs)
+    
+    # Start text generation in a separate thread
+    thread = Thread(target=generate_async)
+    thread.start()
+
+    # Display generated text as it becomes available
+    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 = ""
+            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>"
+                else:
+                    text_tokenwise += 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)
+            generated_text_spanwise = tok.convert_tokens_to_string(annotated_tokens).replace("<|endoftext|>", "")
+
+            output_field.empty()
+            output = f"{css}"
+            output += generated_text_spanwise.replace("\n", " ").replace("$", "$") + "\n<br>"
+            output += "<details><summary>Show tokenwise classification</summary>\n" + text_tokenwise.replace("\n", " ").replace("$", "\\$")
+            #output += "</details><details><summary>Show spans</summary>\n" + text_spans.replace("\n", " ").replace("$", "\\$")
+            if removed_spans != "":
+                output += f"<br><br><i>({removed_spans})</i>"
+            output += "</details>"
+            output_field.write(output, unsafe_allow_html=True)
+
+# Input field
+input_text = st.text_area("Enter prompt for completion", "")
+
+# Sidebar for customizing generation parameters
+with st.sidebar:
+    st.subheader("Generation Parameters")
+    max_new_tokens = st.slider("Max New Tokens", min_value=1, max_value=100, value=30)
+    repetition_penalty = st.slider("Repetition Penalty", min_value=1.0, max_value=2.0, value=1.2)
+    do_sample = st.checkbox("Do Sample", value=True)
+    temperature = st.slider("Temperature", min_value=0.1, max_value=2.0, value=1.0)
+    top_p = st.slider("Top-p", min_value=0.1, max_value=1.0, value=0.3)
+    top_k = st.slider("Top-k", min_value=10, max_value=100, value=50)
+    typical_p = st.slider("Typical P", min_value=0.1, max_value=1.0, value=1.0)
+
+# Button to generate text
+if st.button("Generate"):
+    if input_text:
+        output_field = st.empty()
+        inputs = tok(["  " + input_text], return_tensors="pt").to(model.device)
+        generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=max_new_tokens, 
+                                 repetition_penalty=repetition_penalty, temperature=temperature, 
+                                 top_p=top_p, top_k=top_k, do_sample=do_sample, typical_p=typical_p)
+        generate_text(generation_kwargs, output_field)
+    else:
+        st.warning("Please enter some text first.")