Update Discussion_Buddy.py

Discussion_Buddy.py

@@ -1,264 +1,264 @@
-# Install all of it
-#!pip install python-dotenv
-#!pip install -q -U google-generativeai
-#!pip install -U langchain-community
-#!pip install faiss-cpu
-#!pip install langchain_google_genai
-#!pip install gradio
-# import all library, framework and load all model we need
-from joblib import load
-import gradio as gr
-import re
-import nltk
-from nltk.tokenize.toktok import ToktokTokenizer
-from nltk.stem import LancasterStemmer
-from nltk.corpus import stopwords
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-import torch.nn as nn
-import cv2
-import numpy as np
-from torchvision import models,transforms
-import pickle
-import os
-from dotenv import load_dotenv
-import google.generativeai as genai
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_google_genai import GoogleGenerativeAIEmbeddings,ChatGoogleGenerativeAI
-from langchain_community.vectorstores import FAISS
-from langchain.chains import RetrievalQA
-from langchain_google_genai import GoogleGenerativeAI
-from langchain.chains import ConversationChain
-from langchain.schema import HumanMessage, AIMessage
-from langchain.memory import ConversationBufferMemory
-nltk.download('stopwords')
-# load all model and API
-device = 0 if torch.cuda.is_available() else -1 # check if GPU available using GPU
-translator_eng_id = pipeline('translation', model='Helsinki-NLP/opus-mt-en-id', device = device)# eng-indo
-translator_id_eng = pipeline('translation', model='Helsinki-NLP/opus-mt-id-en', device = device)# indo-eng
-summarizer = pipeline('summarization', model='facebook/bart-large-cnn', device = device) # summarize the news
-ner = pipeline('ner',tokenizer = 'dbmdz/bert-large-cased-finetuned-conll03-english', model='dbmdz/bert-large-cased-finetuned-conll03-english', device = device) # Analyze NER on news
-lang_detector = pipeline('text-classification', model = 'papluca/xlm-roberta-base-language-detection', device = device) #detect language
-news_encoder = load('news_encoder.pkl') #encoder for the news (3 class)
-news_classifier = load("news_classifier.pkl") #classify news into 3 category (Politic, Technology, Science)
-emotion_text_encoder = load("emotion_encoder.pkl") # Encoder for Emotion detection(text version)
-emotion_classifier = load("emotion_model_classifier.pkl") #Detect emotion user based on their comment about news
-with open('class_names_face_recognition.pkl', 'rb') as f: # import and using it as encoder for emotion detection(image version)
-    emotion_face_class = pickle.load(f)
-face_emotion = models.resnet18(pretrained=False) # Detect user emotion based on their expression (selfie)
-face_emotion.fc = nn.Linear(face_emotion.fc.in_features, 5)
-#face_emotion = face_emotion.load_state_dict(torch.load('Best_emotion_face_model.pth'),map_location=torch.device('cpu'))
-device = torch.device("cuda" if torch.cuda.is_available() else 'cpu') # move into GPU if available, so model will using GPU
-state_dict = torch.load('Best_emotion_face_model.pth', map_location=device)
-face_emotion.load_state_dict(state_dict)
-face_emotion.to(device)
-face_emotion.eval()
-# using API Gemini for chatbot
-GOOGLE_API_KEY = os.getenv("GEMINI_API_KEY")
-genai.configure(api_key=GOOGLE_API_KEY)
-
-# Support Function
-#detect language
-def detect_language(text):
-  result = lang_detector(text)
-  return result[0]['label']
-#translate to english if user language not english
-def translate_to_english(text,source_lang):
-  if source_lang.lower() == 'indonesia':
-    return translator_id_eng(text)[0]['translation_text']
-  else:
-    return text
-def translate_to_original(text,target_lang):
-  if target_lang.lower() == 'indonesia':
-    return translator_eng_id(text)[0]['translation_text']
-  else:
-    return text
-# upload reaction
-def process_image_reaction(image_path):
-  image = cv2.imread(image_path)
-  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-  image = image.astype(np.float32)/255.0
-  image = np.transpose(image, (2,0,1))
-  image = torch.tensor(image).unsqueeze(0).to(device)
-  #predict
-  with torch.no_grad():
-    outputs = face_emotion(image)
-    _,predicted = torch.max(outputs,1)
-  return emotion_face_class[predicted.item()]
-#make template  for cleaning
-def cleaning_text(text, is_lower_case = False):
-  text = text.lower()
-  pattern = r'[^a-zA-z0-9\s]'
-  text = re.sub(pattern,'',text)
-  tokenizer = ToktokTokenizer()
-  stopword = stopwords.words('english')
-  tokens = tokenizer.tokenize(text)
-  tokens = [token.strip() for token in tokens]
-  if is_lower_case:
-    filtered_tokens = [token for token in tokens if token not in stopword]
-  else:
-    filtered_tokens = [token for token in tokens if token.lower() not in stopword]
-  filtered_text =' '.join(filtered_tokens)
-  st = LancasterStemmer()
-  text = ' '.join([st.stem(word) for word in filtered_text.split()])
-  return text
-
-#Step 1 to classify, summary and get NER from news
-def extract_article(url):
-   model = genai.GenerativeModel('gemini-1.5-pro')
-   extract = model.generate_content(f"Extract the content from this news article: {url}, make sure extract all news part. Extract based on the news language used, if the news using Indonesia language just extract with Indonesian language, and so on if the news using english language. Make sure minimum is 300 words and maximum is 400 words").text
-   return extract
-def process_news_pipeline(news_text, user_language):
-    news_text = extract_article(news_text)
-    #detect the  language
-    detected_lang = detect_language(news_text)
-#translate news if not in english
-    if detected_lang.lower() != 'english':
-        news_text = translate_to_english(news_text,detected_lang)
-    #analyze the news
-    #classify the news category
-    summary = summarizer(news_text, min_length = 25, do_sample = False)[0]['summary_text']
-    clean_text = cleaning_text(summary)
-    classification = news_classifier.predict([clean_text])[0]
-    classification = news_encoder.inverse_transform([classification])[0]
-    ner_result = ner(news_text)
-    ner_filter = [{"entity":result['entity'],'word':result['word']} for result in ner_result]
-    ner_filter = ner_filter[:5]
-    #translate result into original language if not english
-    if user_language.lower() != 'english':
-        classification = translate_to_original(classification, user_language)
-        summary = translate_to_original(summary, user_language)
-    return classification, summary, ner_filter
-def bring_together_all_of_it(news_text, user_language):
-    news_text = extract_article(news_text)
-    classification, summary, ner_results = process_news_pipeline(news_text,user_language)
-    ner_results = "\n".join([f"Entity: {item['entity']}, Word: {item['word']}" for item in ner_results])
-    return classification, summary, ner_results, news_text
-
-#Step 2 for predict user emotion based on text or image
-def process_user_response(response_type, response_content, user_language):
-    if response_type == "Text" and response_content:
-      sentiment = emotion_text_encoder.inverse_transform([emotion_classifier.predict([response_content])[0]])[0] #predict output using sentiment analysis model
-      if user_language != 'English':
-        translate = translate_to_english(response_content,user_language)
-        sentiment = emotion_text_encoder.inverse_transform([emotion_classifier.predict([translate])[0]])[0] #predict output using sentiment analysis model
-        sentiment = translate_to_original(sentiment,user_language)
-    elif response_type == "Selfie" and response_content:
-      sentiment = process_image_reaction(response_content) # predict output using image classification model
-      if user_language != 'English':
-        sentiment = translate_to_original(sentiment,user_language)
-    else:
-      return "Please provide valid input."
-    return f"Your Expression: {sentiment}"
- 
-#Step 3, make chatbot for discussion about the news
-#load model and memory, to save context while discuss with chatbot
-model = GoogleGenerativeAI(model="gemini-1.5-pro", temperature=0.1)
-memory = ConversationBufferMemory()
-conversation = ConversationChain(
-    llm=model,
-    verbose=False,
-    memory=memory
-)
-def chatbot_interaction(user_message, history, news, user_reaction, user_language):
-  logo = "https://i.ibb.co.com/rwft0JQ/output-removebg-preview-1.png"
-  if history is None:
-    history = []
-  # first response based on bot response to know the context and save it into history
-  if len(history) == 0:
-      initial_response = f"Hi genius! wanna discuss something?"
-      memory.chat_memory.add_ai_message(initial_response)
-      memory.chat_memory.add_user_message(f"Learn and become good presenter based on this news {news}, after that you will discuss with user about this news and make sure using {user_language} language and their ekspression : {user_reaction}")
-      history.append([f"👤 {user_message}", f"<img src='{logo}' style='width:30px; height:30px;'> { initial_response}"])
-      return history, ""
-  memory.chat_memory.add_user_message(user_message)
-  gpt_response = conversation.predict(input=user_message)
-  memory.chat_memory.add_ai_message(gpt_response)
-  history.append([f"👤 {user_message}",f"<img src='{logo}' style='width:30px; height:30px;'> {gpt_response}"] )
-  return history, ""
-
-#Building UI from Gradio
-#Step 1
-with gr.Blocks() as news_analysis_interface:
-  news_text = gr.Textbox(label="Put the news link")
-  user_language = gr.Radio(choices=["English", "Indonesia"], label="User Language")
-  submit_analysis = gr.Button("Analyze News")
-  summary_result = gr.Textbox(label="News Classification", elem_id="summary_result_box")
-  classification_result = gr.Textbox(label="News Summary")
-  ner_result = gr.Textbox(label="Named Entities")
-  news_state = gr.Textbox(label="News")
-  submit_analysis.click(
-      fn=bring_together_all_of_it,
-      inputs=[news_text, user_language],
-      outputs=[summary_result, classification_result, ner_result, news_state]
-  )
-
-#step 2
-with gr.Blocks() as reaction_interface:
-  response_type = gr.Radio(["Text", "Selfie"], label="Response Type")
-  text_input = gr.Textbox(label="Enter Text (only understand english comment)", visible=False)
-  image_input = gr.Image(type="filepath", label="Upload Image", visible=False)
-  reaction_result = gr.Textbox(label="Detected Reaction", interactive=False)
-  def update_visibility(selected_response):
-      if selected_response == "Text":
-          return gr.update(visible=True), gr.update(visible=False)
-      elif selected_response == "Selfie":
-          return gr.update(visible=False), gr.update(visible=True)
-      else:
-          return gr.update(visible=False), gr.update(visible=False)
-  response_type.change(
-      fn=update_visibility,
-      inputs=response_type,
-      outputs=[text_input, image_input]
-  )
-  submit_button = gr.Button("Submit Reaction")
-  def submit_click(response_type_value, text_value, image_value,language_value):
-    if response_type_value == "Text" and text_value:
-      processed_reaction = process_user_response(response_type_value, text_value,language_value)
-      return processed_reaction
-    elif response_type_value == "Selfie" and image_value:
-      processed_reaction = process_user_response(response_type_value, image_value,language_value)
-      return processed_reaction
-    else:
-      return "Please provide valid input."
-  submit_button.click(
-      fn = submit_click,
-      inputs=[response_type, text_input, image_input, user_language],
-      outputs=[reaction_result]
-  )
-
-#step 3
-with gr.Blocks() as chatbot_interface:
-  chatbot_chatbox = gr.Chatbot(label="Chatbot Interaction")
-  chatbot_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
-  chatbot_submit_button = gr.Button("Send")
-  chatbot_history = gr.State(value=[])
-  chatbot_submit_button.click(
-      fn=chatbot_interaction,
-      inputs=[chatbot_input,chatbot_history,news_state,reaction_result,user_language],
-      outputs=[chatbot_chatbox,chatbot_input]
-  )
-  chatbot_input.submit(fn=chatbot_interaction,
-                        inputs=[chatbot_input,chatbot_history,news_state,reaction_result,user_language],
-      outputs=[chatbot_chatbox,chatbot_input]
-  )
-
-#Bring it all and launch
-with gr.Blocks(theme=gr.themes.Default(primary_hue='gray', secondary_hue='zinc')) as full_app:
-  with gr.Column():
-    # Membuat gambar terpusat di atas
-    gr.HTML("""
-      <div style="text-align: center;">
-          <img src="https://i.ibb.co.com/rwft0JQ/output-removebg-preview-1.png" alt="Robot Logo" style="width:150px; display: block; margin: 0 auto;">
-      </div>
-      """)
-    # Menambahkan judul
-    gr.Markdown("<h1 style='text-align: center;'>Your Discussion Friend</h1>")
-  #gr.Markdown("# Your Discussion Friend")
-  gr.Markdown("**Step 1**: Analyze the News")
-  news_analysis_interface.render()
-  gr.Markdown("**Step 2**: Choose Your Reaction")
-  reaction_interface.render()
-  gr.Markdown("**Step 3**: Interact with Chatbot")
-  chatbot_interface.render()
-full_app.launch(server_name="0.0.0.0", server_port=7860)
+# Install all of it
+#!pip install python-dotenv
+#!pip install -q -U google-generativeai
+#!pip install -U langchain-community
+#!pip install faiss-cpu
+#!pip install langchain_google_genai
+#!pip install gradio
+# import all library, framework and load all model we need
+from joblib import load
+import gradio as gr
+import re
+import nltk
+from nltk.tokenize.toktok import ToktokTokenizer
+from nltk.stem import LancasterStemmer
+from nltk.corpus import stopwords
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+import torch.nn as nn
+import cv2
+import numpy as np
+from torchvision import models,transforms
+import pickle
+import os
+from dotenv import load_dotenv
+import google.generativeai as genai
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_google_genai import GoogleGenerativeAIEmbeddings,ChatGoogleGenerativeAI
+from langchain_community.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain_google_genai import GoogleGenerativeAI
+from langchain.chains import ConversationChain
+from langchain.schema import HumanMessage, AIMessage
+from langchain.memory import ConversationBufferMemory
+nltk.download('stopwords')
+# load all model and API
+device = 0 if torch.cuda.is_available() else -1 # check if GPU available using GPU
+translator_eng_id = pipeline('translation', model='Helsinki-NLP/opus-mt-en-id', device = device)# eng-indo
+translator_id_eng = pipeline('translation', model='Helsinki-NLP/opus-mt-id-en', device = device)# indo-eng
+summarizer = pipeline('summarization', model='facebook/bart-large-cnn', device = device) # summarize the news
+ner = pipeline('ner',tokenizer = 'dbmdz/bert-large-cased-finetuned-conll03-english', model='dbmdz/bert-large-cased-finetuned-conll03-english', device = device) # Analyze NER on news
+lang_detector = pipeline('text-classification', model = 'papluca/xlm-roberta-base-language-detection', device = device) #detect language
+news_encoder = load('news_encoder.pkl') #encoder for the news (3 class)
+news_classifier = load("news_classifier.pkl") #classify news into 3 category (Politic, Technology, Science)
+emotion_text_encoder = load("emotion_encoder.pkl") # Encoder for Emotion detection(text version)
+emotion_classifier = load("emotion_model_classifier.pkl") #Detect emotion user based on their comment about news
+with open('class_names_face_recognition.pkl', 'rb') as f: # import and using it as encoder for emotion detection(image version)
+    emotion_face_class = pickle.load(f)
+face_emotion = models.resnet18(pretrained=False) # Detect user emotion based on their expression (selfie)
+face_emotion.fc = nn.Linear(face_emotion.fc.in_features, 5)
+#face_emotion = face_emotion.load_state_dict(torch.load('Best_emotion_face_model.pth'),map_location=torch.device('cpu'))
+device = torch.device("cuda" if torch.cuda.is_available() else 'cpu') # move into GPU if available, so model will using GPU
+state_dict = torch.load('Best_emotion_face_model.pth', map_location=device)
+face_emotion.load_state_dict(state_dict)
+face_emotion.to(device)
+face_emotion.eval()
+# using API Gemini for chatbot
+GOOGLE_API_KEY = os.getenv("GEMINI_API_KEY")
+genai.configure(api_key=GOOGLE_API_KEY)
+
+# Support Function
+#detect language
+def detect_language(text):
+  result = lang_detector(text)
+  return result[0]['label']
+#translate to english if user language not english
+def translate_to_english(text,source_lang):
+  if source_lang.lower() == 'indonesia':
+    return translator_id_eng(text)[0]['translation_text']
+  else:
+    return text
+def translate_to_original(text,target_lang):
+  if target_lang.lower() == 'indonesia':
+    return translator_eng_id(text)[0]['translation_text']
+  else:
+    return text
+# upload reaction
+def process_image_reaction(image_path):
+  image = cv2.imread(image_path)
+  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+  image = image.astype(np.float32)/255.0
+  image = np.transpose(image, (2,0,1))
+  image = torch.tensor(image).unsqueeze(0).to(device)
+  #predict
+  with torch.no_grad():
+    outputs = face_emotion(image)
+    _,predicted = torch.max(outputs,1)
+  return emotion_face_class[predicted.item()]
+#make template  for cleaning
+def cleaning_text(text, is_lower_case = False):
+  text = text.lower()
+  pattern = r'[^a-zA-z0-9\s]'
+  text = re.sub(pattern,'',text)
+  tokenizer = ToktokTokenizer()
+  stopword = stopwords.words('english')
+  tokens = tokenizer.tokenize(text)
+  tokens = [token.strip() for token in tokens]
+  if is_lower_case:
+    filtered_tokens = [token for token in tokens if token not in stopword]
+  else:
+    filtered_tokens = [token for token in tokens if token.lower() not in stopword]
+  filtered_text =' '.join(filtered_tokens)
+  st = LancasterStemmer()
+  text = ' '.join([st.stem(word) for word in filtered_text.split()])
+  return text
+
+#Step 1 to classify, summary and get NER from news
+def extract_article(url):
+   model = genai.GenerativeModel('gemini-1.5-pro')
+   extract = model.generate_content(f"Extract the content from this news article: {url}, make sure extract all news part. Extract based on the news language used, if the news using Indonesia language just extract with Indonesian language, and so on if the news using english language. Make sure minimum is 300 words and maximum is 400 words").text
+   return extract
+def process_news_pipeline(news_text, user_language):
+    news_text = extract_article(news_text)
+    #detect the  language
+    detected_lang = detect_language(news_text)
+#translate news if not in english
+    if detected_lang.lower() != 'english':
+        news_text = translate_to_english(news_text,detected_lang)
+    #analyze the news
+    #classify the news category
+    summary = summarizer(news_text, min_length = 25, do_sample = False)[0]['summary_text']
+    clean_text = cleaning_text(summary)
+    classification = news_classifier.predict([clean_text])[0]
+    classification = news_encoder.inverse_transform([classification])[0]
+    ner_result = ner(news_text)
+    ner_filter = [{"entity":result['entity'],'word':result['word']} for result in ner_result]
+    ner_filter = ner_filter[:5]
+    #translate result into original language if not english
+    if user_language.lower() != 'english':
+        classification = translate_to_original(classification, user_language)
+        summary = translate_to_original(summary, user_language)
+    return classification, summary, ner_filter
+def bring_together_all_of_it(news_text, user_language):
+    news_text = extract_article(news_text)
+    classification, summary, ner_results = process_news_pipeline(news_text,user_language)
+    ner_results = "\n".join([f"Entity: {item['entity']}, Word: {item['word']}" for item in ner_results])
+    return classification, summary, ner_results, news_text
+
+#Step 2 for predict user emotion based on text or image
+def process_user_response(response_type, response_content, user_language):
+    if response_type == "Text" and response_content:
+      sentiment = emotion_text_encoder.inverse_transform([emotion_classifier.predict([response_content])[0]])[0] #predict output using sentiment analysis model
+      if user_language != 'English':
+        translate = translate_to_english(response_content,user_language)
+        sentiment = emotion_text_encoder.inverse_transform([emotion_classifier.predict([translate])[0]])[0] #predict output using sentiment analysis model
+        sentiment = translate_to_original(sentiment,user_language)
+    elif response_type == "Selfie" and response_content:
+      sentiment = process_image_reaction(response_content) # predict output using image classification model
+      if user_language != 'English':
+        sentiment = translate_to_original(sentiment,user_language)
+    else:
+      return "Please provide valid input."
+    return f"Your Expression: {sentiment}"
+ 
+#Step 3, make chatbot for discussion about the news
+#load model and memory, to save context while discuss with chatbot
+model = GoogleGenerativeAI(model="gemini-1.5-pro", temperature=0.1)
+memory = ConversationBufferMemory()
+conversation = ConversationChain(
+    llm=model,
+    verbose=False,
+    memory=memory
+)
+def chatbot_interaction(user_message, history, news, user_reaction, user_language):
+  logo = "https://i.ibb.co.com/rwft0JQ/output-removebg-preview-1.png"
+  if history is None:
+    history = []
+  # first response based on bot response to know the context and save it into history
+  if len(history) == 0:
+      initial_response = f"Hi genius! wanna discuss something?"
+      memory.chat_memory.add_ai_message(initial_response)
+      memory.chat_memory.add_user_message(f"Learn and become good presenter based on this news {news}, after that you will discuss with user about this news and make sure using {user_language} language and their ekspression : {user_reaction}")
+      history.append([f"👤 {user_message}", f"<img src='{logo}' style='width:30px; height:30px;'> { initial_response}"])
+      return history, ""
+  memory.chat_memory.add_user_message(user_message)
+  gpt_response = conversation.predict(input=user_message)
+  memory.chat_memory.add_ai_message(gpt_response)
+  history.append([f"👤 {user_message}",f"<img src='{logo}' style='width:30px; height:30px;'> {gpt_response}"] )
+  return history, ""
+
+#Building UI from Gradio
+#Step 1
+with gr.Blocks() as news_analysis_interface:
+  news_text = gr.Textbox(label="Put the news link")
+  user_language = gr.Radio(choices=["English", "Indonesia"], label="User Language")
+  submit_analysis = gr.Button("Analyze News")
+  summary_result = gr.Textbox(label="News Classification", elem_id="summary_result_box")
+  classification_result = gr.Textbox(label="News Summary")
+  ner_result = gr.Textbox(label="Named Entities")
+  news_state = gr.Textbox(label="News")
+  submit_analysis.click(
+      fn=bring_together_all_of_it,
+      inputs=[news_text, user_language],
+      outputs=[summary_result, classification_result, ner_result, news_state]
+  )
+
+#step 2
+with gr.Blocks() as reaction_interface:
+  response_type = gr.Radio(["Text", "Selfie"], label="Response Type")
+  text_input = gr.Textbox(label="Enter Text (only understand english comment)", visible=False)
+  image_input = gr.Image(type="filepath", label="Upload Image", visible=False)
+  reaction_result = gr.Textbox(label="Detected Reaction", interactive=False)
+  def update_visibility(selected_response):
+      if selected_response == "Text":
+          return gr.update(visible=True), gr.update(visible=False)
+      elif selected_response == "Selfie":
+          return gr.update(visible=False), gr.update(visible=True)
+      else:
+          return gr.update(visible=False), gr.update(visible=False)
+  response_type.change(
+      fn=update_visibility,
+      inputs=response_type,
+      outputs=[text_input, image_input]
+  )
+  submit_button = gr.Button("Submit Reaction")
+  def submit_click(response_type_value, text_value, image_value,language_value):
+    if response_type_value == "Text" and text_value:
+      processed_reaction = process_user_response(response_type_value, text_value,language_value)
+      return processed_reaction
+    elif response_type_value == "Selfie" and image_value:
+      processed_reaction = process_user_response(response_type_value, image_value,language_value)
+      return processed_reaction
+    else:
+      return "Please provide valid input."
+  submit_button.click(
+      fn = submit_click,
+      inputs=[response_type, text_input, image_input, user_language],
+      outputs=[reaction_result]
+  )
+
+#step 3
+with gr.Blocks() as chatbot_interface:
+  chatbot_chatbox = gr.Chatbot(label="Chatbot Interaction")
+  chatbot_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
+  chatbot_submit_button = gr.Button("Send")
+  chatbot_history = gr.State(value=[])
+  chatbot_submit_button.click(
+      fn=chatbot_interaction,
+      inputs=[chatbot_input,chatbot_history,news_state,reaction_result,user_language],
+      outputs=[chatbot_chatbox,chatbot_input]
+  )
+  chatbot_input.submit(fn=chatbot_interaction,
+                        inputs=[chatbot_input,chatbot_history,news_state,reaction_result,user_language],
+      outputs=[chatbot_chatbox,chatbot_input]
+  )
+
+#Bring it all and launch
+with gr.Blocks(theme=gr.themes.Default(primary_hue='gray', secondary_hue='zinc')) as full_app:
+  with gr.Column():
+    # Membuat gambar terpusat di atas
+    gr.HTML("""
+      <div style="text-align: center;">
+          <img src="https://i.ibb.co.com/rwft0JQ/output-removebg-preview-1.png" alt="Robot Logo" style="width:150px; display: block; margin: 0 auto;">
+      </div>
+      """)
+    # Menambahkan judul
+    gr.Markdown("<h1 style='text-align: center;'>Your Discussion Friend</h1>")
+  #gr.Markdown("# Your Discussion Friend")
+  gr.Markdown("**Step 1**: Analyze the News")
+  news_analysis_interface.render()
+  gr.Markdown("**Step 2**: Choose Your Reaction")
+  reaction_interface.render()
+  gr.Markdown("**Step 3**: Interact with Chatbot")
+  chatbot_interface.render()
+full_app.launch()