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app.py
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app.py
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import streamlit as st
from langchain_community.callbacks.streamlit import StreamlitCallbackHandler
from src.bharatchat.chatbot import BharatChatAI, ChatHandler, ToolsAndAgentsInitializer
class StreamlitInterface:
def __init__(self, chat_ai_instance):
self.chat_ai = chat_ai_instance
self.translations = self._load_translations()
if 'language' not in st.session_state:
st.session_state.language = 'en'
self.current_language = st.session_state.language
def _load_translations(self):
return {
'en': {
'title': 'Bharat AI ChatBot',
'chatbot_selection': 'Choose a Chatbot:',
'model_selection': '🔍 Choose a Model',
'memory_length': '🧠 Conversational Memory Length:',
'temperature': '🌡️ Temperature:',
'max_tokens': '🧩 Max Tokens:',
'clear_history': '🗑️ Clear Chat History',
'url_input': 'Enter the URL of the document:',
'upload_files': 'Upload your files',
'input_placeholder': 'Input your question here',
'chatbot_summaries': {
'QA Chatbot': 'The QA Chatbot engages in a question-and-answer session, providing accurate and relevant responses to your queries.',
'Chat with PDF': 'The Chat with PDF option allows you to upload a PDF document and interact with its content to extract useful information.',
'Chat with URL': 'The Chat with URL feature lets you enter a URL of a document to interact with its content and obtain insights.'
},
'no_content_found': 'No content found in the response.',
'unexpected_format': 'Response is not in the expected format.',
'error_message': 'An error occurred: {}',
'initializing_chat_handler': 'Chat handler is not initialized. Initializing now.',
'content_processed': 'Content processed successfully!'
},
'hi': {
'title': 'भारत एआई चैटबॉट',
'chatbot_selection': 'चैटबॉट चुनें:',
'model_selection': '🔍 मॉडल चुनें',
'memory_length': '🧠 वार्तालाप की मेमोरी लंबाई:',
'temperature': '🌡️ तापमान:',
'max_tokens': '🧩 अधिकतम टोकन:',
'clear_history': '🗑️ चैट इतिहास साफ़ करें',
'url_input': 'दस्तावेज़ के URL को दर्ज करें:',
'upload_files': 'अपनी फ़ाइलें अपलोड करें',
'input_placeholder': 'यहां अपना सवाल दर्ज करें',
'chatbot_summaries': {
'QA Chatbot': 'QA चैटबॉट प्रश्नोत्तर सत्र में संलग्न होता है, आपकी पूछताछों के सटीक और प्रासंगिक उत्तर प्रदान करता है।',
'Chat with PDF': 'PDF के साथ चैट विकल्प आपको एक PDF दस्तावेज़ अपलोड करने और इसके सामग्री के साथ बातचीत करने की अनुमति देता है।',
'Chat with URL': 'URL के साथ चैट सुविधा आपको दस्तावेज़ के URL को दर्ज करने और इसके सामग्री के साथ बातचीत करने की अनुमति देती है।'
},
'no_content_found': 'प्रतिक्रिया में कोई सामग्री नहीं मिली।',
'unexpected_format': 'प्रतिक्रिया अपेक्षित प्रारूप में नहीं है।',
'error_message': 'एक त्रुटि हुई: {}',
'initializing_chat_handler': 'चैट हैंडलर शुरू नहीं किया गया है। अभी शुरू हो रहा है।',
'content_processed': 'सामग्री सफलतापूर्वक संसाधित की गई!'
}
}
def render_app(self):
"""Render the Streamlit app interface."""
if 'chat_option' not in st.session_state:
st.session_state.chat_option = "QA Chatbot"
if 'chat_histories' not in st.session_state:
st.session_state.chat_histories = {option: [] for option in ["QA Chatbot", "Chat with PDF", "Chat with URL"]}
if 'vectors' not in st.session_state:
st.session_state.vectors = None
self.current_language = st.session_state.language
self._set_background_image()
self.display_logo_and_title()
self._display_chatbot_summary() # Display summary based on selected chatbot
self._initialize_sidebar()
self._handle_sidebar_selection()
def _set_background_image(self):
"""Set the background image for the app."""
st.markdown(
"""
<style>
.stApp {
background-image: url("https://example.com/your-background-image.jpg");
background-size: cover;
background-position: center;
}
</style>
""",
unsafe_allow_html=True
)
def display_logo_and_title(self):
"""Display the application logo and title side by side with tricolor effect."""
spacer, col = st.columns([5, 1])
with col:
st.image('templates/groqcloud.png', width=100) # Adjust width as needed
spacer.markdown(
f"""
<div style="font-size: 60px; font-weight: bold; background: linear-gradient(to right, #FF9933, #FFFFFF, #138808); -webkit-background-clip: text; -webkit-text-fill-color: transparent; display: inline;">
{self.translations[self.current_language]['title']}
</div>
<span style="font-size: 80px;">🇮🇳</span>
""",
unsafe_allow_html=True
)
def _initialize_sidebar(self):
"""Initialize the sidebar with customization options."""
st.sidebar.title('TweakIt 🎛️')
# Language selection
languages = ['en', 'hi']
selected_language = st.sidebar.selectbox(
'Select Language',
languages,
index=languages.index(st.session_state.get('language', 'en')),
format_func=lambda x: 'English' if x == 'en' else 'हिन्दी'
)
if selected_language != st.session_state.get('language', 'en'):
st.session_state.language = selected_language
self.current_language = selected_language
# Reset chat option and histories if language changes
st.session_state.chat_option = "QA Chatbot"
st.session_state.chat_histories = {option: [] for option in ["QA Chatbot", "Chat with PDF", "Chat with URL"]}
st.session_state.vectors = None
# Ensure that we don't update session state after widget creation
chat_options = ["QA Chatbot", "Chat with PDF", "Chat with URL"]
if 'chat_option' not in st.session_state:
st.session_state.chat_option = "QA Chatbot"
selected_option = st.sidebar.radio(
self.translations[self.current_language]['chatbot_selection'],
chat_options,
index=chat_options.index(st.session_state.chat_option),
key="chat_option"
)
# Selectbox for choosing a model
st.sidebar.selectbox(
self.translations[self.current_language]['model_selection'],
self.chat_ai._get_model_options(),
help="Select the AI model you want to use."
)
# Slider for conversational memory length
memory_length = st.sidebar.slider(
self.translations[self.current_language]['memory_length'],
1, 10,
value=st.session_state.get('memory_length', 5),
help="Set how many previous interactions the chatbot should remember."
)
st.session_state.memory_length = memory_length
# Slider for temperature
temperature = st.sidebar.slider(
self.translations[self.current_language]['temperature'],
0.0, 1.0,
value=st.session_state.get('temperature', 0.7),
step=0.1,
help="Adjust the randomness of the chatbot's responses."
)
st.session_state.temperature = temperature
# Slider for max tokens
max_tokens = st.sidebar.slider(
self.translations[self.current_language]['max_tokens'],
50, 1000,
value=st.session_state.get('max_tokens', 300),
step=50,
help="Specify the maximum number of tokens for responses."
)
st.session_state.max_tokens = max_tokens
# Button to clear chat history
if st.sidebar.button(self.translations[self.current_language]['clear_history']):
st.session_state.chat_histories[st.session_state.chat_option] = []
st.success("✅ Chat history cleared!")
def _handle_sidebar_selection(self):
"""Handle user selection from the sidebar and initialize tools and agents."""
chat_option = st.session_state.get("chat_option", "QA Chatbot")
selected_model = st.session_state.get("_MODEL", "default_model") # Assuming default model if not set
if selected_model != st.session_state.get("_MODEL"):
st.session_state._MODEL = selected_model
# Initialize tools and agents
tools_and_agents_initializer = ToolsAndAgentsInitializer(model=selected_model, language=self.current_language)
self.chat_ai.search_agent = tools_and_agents_initializer.initialize_tools_and_agents()
if chat_option == "Chat with URL":
self._handle_url_chat()
elif chat_option == "QA Chatbot":
self._handle_qa_chat()
elif chat_option == "Chat with PDF":
self._handle_pdf_chat()
else:
st.warning("Please select a chat option to get started.")
def _initialize_chat_handler(self):
"""Initialize the chat handler if it is not already initialized."""
if not self.chat_ai.chat_handler:
try:
# Initialize the chat handler without passing additional arguments
self.chat_ai.chat_handler = ChatHandler(st.session_state.vectors)
except Exception as e:
st.error(f"Failed to initialize chat handler: {e}")
return False
return True
def _handle_qa_chat(self):
query = st.chat_input(placeholder=self.translations[self.current_language]['input_placeholder'])
if query:
if not self._initialize_chat_handler():
return
st.session_state.chat_histories[st.session_state.chat_option].append({"role": "user", "content": query})
self.chat_ai.chat_handler._display_chat_history()
self._display_chat_response()
def _handle_pdf_chat(self):
uploaded_file = st.file_uploader(self.translations[self.current_language]['upload_files'], type=["pdf", "txt"])
if uploaded_file:
# Initialize ChatHandler if not already done
if not self._initialize_chat_handler():
return
# Process the uploaded file
self.chat_ai.document_processor.process_documents(uploaded_file)
# Display content processed message if it hasn't been displayed yet
if not st.session_state.get('content_processed_displayed', False):
st.success(self.translations[self.current_language]['content_processed'])
st.session_state.content_processed_displayed = False
# Handle chat input
query = st.chat_input(placeholder=self.translations[self.current_language]['input_placeholder'])
if query:
self.chat_ai.chat_handler.handle_chat(query)
# Clear the flag after handling chat
st.session_state.content_processed_displayed = False
def _handle_url_chat(self):
url = st.text_input(self.translations[self.current_language]['url_input'])
if url:
# Initialize ChatHandler if not already done
if not self._initialize_chat_handler():
return
# Process the URL
self.chat_ai.document_processor.process_url(url)
# Display content processed message if it hasn't been displayed yet
if not st.session_state.get('content_processed_displayed', False):
st.success(self.translations[self.current_language]['content_processed'])
st.session_state.content_processed_displayed = True
# Handle chat input
query = st.chat_input(placeholder=self.translations[self.current_language]['input_placeholder'])
if query:
self.chat_ai.chat_handler.handle_chat(query)
# Clear the flag after handling chat
st.session_state.content_processed_displayed = False
def _handle_chat_input(self):
"""Handle chat input from the user and display response."""
user_input = st.text_input(self.translations[self.current_language]['input_placeholder'])
if st.button("Send"):
if not self.chat_ai.search_agent:
st.warning(self.translations[self.current_language]['error_message'].format("Search agent is not initialized."))
return
if not self.chat_ai.chat_handler:
self.chat_ai.chat_handler = ChatHandler(st.session_state.vectors)
if not self.chat_ai.chat_handler:
st.warning(self.translations[self.current_language]['error_message'].format("Failed to initialize ChatHandler."))
return
st.session_state.chat_histories[st.session_state.chat_option].append({"role": "user", "content": user_input})
self.chat_ai.chat_handler._display_chat_history()
self._display_chat_response()
def _display_chat_response(self):
with st.chat_message("assistant"):
st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
try:
if not self.chat_ai.search_agent:
st.warning(self.translations[self.current_language]['error_message'].format("Search agent is not initialized."))
return
response = self.chat_ai.search_agent.run(
st.session_state.chat_histories[st.session_state.chat_option],
callbacks=[st_cb]
)
st.session_state.chat_histories[st.session_state.chat_option].append({'role': 'assistant', "content": response})
# Ensure response translation based on the current language
if isinstance(response, str):
st.write(response)
elif isinstance(response, list):
content = [entry['content'] for entry in response if entry.get('role') == 'assistant']
if content:
st.write(content[0])
else:
st.write(self.translations[self.current_language]['no_content_found'])
else:
st.write(self.translations[self.current_language]['unexpected_format'])
except Exception as e:
st.error(self.translations[self.current_language]['error_message'].format(e))
def _display_chatbot_summary(self):
"""Display the summary of the selected chatbot."""
chat_option = st.session_state.chat_option
summary = self.translations[self.current_language]['chatbot_summaries'].get(chat_option, "Select a chatbot to see its summary.")
st.markdown(f"### {chat_option}")
st.write(summary)
def run_streamlit_app(self):
"""Run the Streamlit app interface."""
StreamlitInterface(self).render_app()
# Streamlit app execution
if __name__ == "__main__":
interface = StreamlitInterface(BharatChatAI())
interface.render_app()