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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data/test.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,28 @@
----
-title: Dialogue Summarization
-emoji: 📚
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 4.36.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: dialogue-summarization
+app_file: demo.py
+sdk: gradio
+sdk_version: 4.36.1
+---
+# dialogue-summarization
+A mini-project topic at Viettel Digital Talent 2024
+
+## How to use: 
+To use this project using git, please do the following.
+* Firstly, clone the repository's main branch into your desired directory using your git command prompt.
+
+```git clone https://github.com/king17pvp/dialogue-summarization.git```
+* Secondly, you can access the directory by this command.
+
+```cd dialogue-summarization```
+
+* Thirdly, install required libraries via requirements.txt
+
+```pip install -q -r requirements.txt```
+* Finally, run the project by 
+
+```python demo.py```
+* If you want to stop the program, press *Ctrl + C* in the terminal, the session will stop.
+
+* **IMPORTANT NOTES** If there are any errors when you run the project via step 3, you should reinstall Python from the official website with version 3.10+ and restart from step 1. The reference link to download python: https://www.python.org/downloads/release/python-3123/ (Make sure to add Python to PATH when installed)
+

data/hf_tokens.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0f9adedca61139d46f323d18a929f71424fa3931e3eee560c64a3a3484db290
+size 218

data/test.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a26c61f1ac554e654bd4e439324a3a3caab5269050c8e378c1a4e615124e44bc
+size 13393961

demo.py

@@ -0,0 +1,72 @@
+import gradio as gr
+import requests 
+import nltk
+import json
+import random
+from utils import *
+
+# Function to handle summarization based on the selected method
+def summarize(text: str, method: str) -> str:
+    processed_text = ' '.join(text.split("\n"))
+    chunks = split_chunk(text)
+    tree = MemWalker(chunks)
+    tree.build_memory_tree()
+    summary = summarize_three_ways(chunks)
+    
+    #print(processed_text)
+
+    if method == "Truncation":
+        return summary['truncated']
+    elif method == "Rewrite":
+        return summary['rewrite']
+    elif method == "Accumulate":
+        return summary['accumulate']
+    elif method == "Memory Tree":
+        return tree.root.summary
+
+example_transcript = get_example()
+
+def clear_input():
+    return "", "Truncation"
+
+def load_example(index):
+    return example_transcript[index]
+
+def load_example_1():
+    return load_example(0)
+def load_example_2():
+    return load_example(1)
+def load_example_3():
+    return load_example(2)
+def load_example_4():
+    return load_example(3)
+
+# Create the interface
+with gr.Blocks() as iface:
+    text_input = gr.Textbox(lines=5, placeholder="Enter text to summarize here...", label="Input Transcript")
+    method_input = gr.Radio(choices=["Truncation", "Rewrite", "Accumulate", "Memory Tree"], label="Summarization Method", value="Truncation")
+    output_text = gr.Textbox(label="Summary")
+
+    summarize_button = gr.Button("Summarize")
+    clear_button = gr.Button("Clear Input")
+
+    summarize_button.click(summarize, inputs=[text_input, method_input], outputs=output_text)
+    clear_button.click(clear_input, outputs=[text_input, method_input])
+    #gr.Exa
+    #gr.Examples([example_transcript[:100]], inputs=[text_input], label=f"Example", fn=load_example)
+    
+    example_button_1 = gr.Button(f"Example {1}: {example_transcript[0][:600]}...")
+    example_button_1.click(load_example_1, inputs = None, outputs=[text_input])
+    
+    example_button_2 = gr.Button(f"Example {2}: {example_transcript[1][:600]}...")
+    example_button_2.click(load_example_2, inputs = None, outputs=[text_input])
+    
+    example_button_3 = gr.Button(f"Example {3}: {example_transcript[2][:600]}...")
+    example_button_3.click(load_example_3, inputs = None, outputs=[text_input])
+    
+    example_button_4 = gr.Button(f"Example {4}: {example_transcript[3][:600]}...")
+    example_button_4.click(load_example_4, inputs = None, outputs=[text_input])
+    
+
+# Launch the interface
+iface.launch(share = True, debug = True)

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+nltk
+requests

utils.py

@@ -0,0 +1,180 @@
+import requests 
+import nltk
+import random
+import json
+import os
+import pickle
+import re
+hf_tokens = []
+
+filepath = __file__.replace("\\", "/").replace("utils.py", "")
+
+with open(filepath + "data/hf_tokens.pkl", "rb") as f:
+    hf_tokens = pickle.load(f)
+
+
+MAX_TOKEN_LENGTH = 4096
+MAX_CHUNK_SIZE = 16000
+API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
+
+def prompt_template(prompt, sys_prompt = ""):
+    return_prompt = '<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<system_prompt><|eot_id|><|start_header_id|>user<|end_header_id|>\n\n<user_prompt><|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'.replace('<user_prompt>', prompt).replace('<system_prompt>', sys_prompt)
+    return return_prompt
+
+def query(payload: dict, hf_token: str):
+    headers = {"Authorization": f"Bearer {hf_token}"}
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
+
+def gen_prompt(prompt: str, sys_prompt:str = ""):
+    input_prompt = prompt_template(prompt, sys_prompt)
+    selected_token = ''
+    for token in hf_tokens:
+        test_output = query({
+            "inputs": prompt_template("Who are you?"),
+            "parameters": {"max_new_tokens": 100}
+        }, token)
+        if 'error' not in test_output:
+            selected_token = token
+            break
+    output = query({
+        "inputs": input_prompt,
+        "parameters": {"max_new_tokens": 512},
+    }, selected_token)
+    return output[0]['generated_text'][len(input_prompt):]
+
+class Node:
+    def __init__(self, summary=None):
+        self.summary = summary
+        self.children = []
+        self.parent = None
+
+    def add_child(self, child_node):
+        child_node.parent = self
+        self.children.append(child_node)
+
+class MemWalker:
+    def __init__(self, segments):
+        self.segments = segments
+        self.root = 0
+
+    def build_memory_tree(self):
+        # Step 1: Create leaf nodes for each segment
+        leaves = [Node(summarize(seg, 0)) for seg in self.segments]
+        
+        # Step 2: Build tree recursively
+        while len(leaves) > 1:
+            new_leaves = []
+            for i in range(0, len(leaves), 2):
+                if i + 1 < len(leaves):
+                    combined_summary = summarize(leaves[i].summary + ", " + leaves[i + 1].summary, 1)
+                    parent_node = Node(combined_summary)
+                    parent_node.add_child(leaves[i])
+                    parent_node.add_child(leaves[i + 1])
+                else:
+                    parent_node = leaves[i]
+                new_leaves.append(parent_node)
+            leaves = new_leaves
+        self.root = leaves[0]
+
+# Placeholder functions for LLM operations
+def summarize(text, sum_type: int = 1):
+    assert sum_type in [0, 1], "Lmao sum type should be either 0 or 1"
+    if sum_type == 0:
+        USER_PROMPT = "Write a concise summary of the meeting transcript in maximum 5 sentences:" + "\n\n" + text
+    else: 
+        USER_PROMPT = "Compress the following summaries into a much shorter summary: " + "\n\n" + text
+    SYS_PROMPT = "Act as a professional technical meeting minutes writer."
+    
+    tmp = gen_prompt(USER_PROMPT, SYS_PROMPT)
+    if len(tmp.split("\n\n")) == 1:
+        return tmp 
+    else:
+        return tmp.split("\n\n")[1]
+
+
+    #return output[0]['generated_text'][len(input_prompt):]
+
+def split_chunk(transcript: str):
+    sentences = nltk.sent_tokenize(transcript)
+    idx = 0
+    chunk = []
+    current_chunk = ""
+    while idx < len(sentences):
+        if len(current_chunk + sentences[idx]) < MAX_CHUNK_SIZE:
+            current_chunk += sentences[idx] + " "
+        else:
+            chunk.append(current_chunk)
+            current_chunk = ''
+            for i in range(10, -1, -1):
+                current_chunk += sentences[idx - i] + " "
+        idx += 1
+    
+    chunk.append(current_chunk)
+    
+    return chunk
+
+def summarize_three_ways(chunks: list[str]):
+    
+    SYS_PROMPT = "Act as a professional technical meeting minutes writer."
+    PROMPT_TEMPLATE = "Write a concise summary of the meeting transcript in maximum 5 sentences:" + "\n\n" + "{text}"
+    REFINE_TEMPLATE = (
+    "Your job is to produce a final summary\n"
+    "We have provided an existing summary up to a certain point: {existing_answer}\n"
+    "We have the opportunity to refine the existing summary"
+    "(only if needed) with some more context below.\n"
+    "------------\n"
+    "{text}\n"
+    "------------\n"
+    f"Given the new context, refine the original summary in English within 5 sentences. If the context isn't useful, return the original summary."       
+    )
+    step = 0
+    prev_sum = ""
+    partial_sum = []
+    return_dict = {}
+    for chunk in chunks:
+        if step == 0:
+            CUR_PROMPT = PROMPT_TEMPLATE.replace("{text}", chunk)
+            cur_sum = gen_prompt(CUR_PROMPT , SYS_PROMPT)     
+        else:
+            CUR_PROMPT = REFINE_TEMPLATE.replace("{existing_answer}", partial_sum[-1])
+            CUR_PROMPT = CUR_PROMPT.replace("{text}", chunk)
+            cur_sum = gen_prompt(CUR_PROMPT, SYS_PROMPT)
+        if len(cur_sum.split("\n\n")) > 1:
+            cur_sum = cur_sum.split("\n\n")[1]
+        #print(cur_sum)
+        partial_sum.append(cur_sum)
+        step += 1
+    #print(partial_sum)
+    CUR_PROMPT = "Rewrite the following text by maintaining coherency: " + "\n\n"
+    CUR_PROMPT += ' '.join(partial_sum)
+    tmp = gen_prompt(CUR_PROMPT, SYS_PROMPT)
+    final_sum = ''
+    if len(tmp.split("\n\n")) == 1:
+        final_sum = tmp 
+    else:
+        final_sum = tmp.split("\n\n")[1]
+    return_dict['truncated'] = partial_sum[0]
+    return_dict['accumulate'] = partial_sum[-1]
+    return_dict['rewrite'] = final_sum
+    
+    return return_dict
+
+def get_example()->list[str]:
+    data = []
+    with open(filepath + "data/test.json", "r") as f:
+        for line in f:
+            data.append(json.loads(line))
+    
+    #random_idx = random.sample(list(range(len(data))), 6)
+    random_idx = [1, 2, 9, 13]
+    return ['\n'.join(nltk.sent_tokenize(data[i]['transcript'])) for i in random_idx]
+
+if __name__ == "__main__":
+    '''data = []
+    with open(filepath + "data/test.json", "r") as f:
+        for line in f:
+            data.append(json.loads(line))
+    tmp = data[:100]
+    for j, i in enumerate(tmp):
+        print(j, len(i['transcript']))'''