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Update app.py
Browse files
app.py
CHANGED
@@ -1,129 +1,52 @@
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import os
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import gradio as gr
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from huggingface_hub import InferenceClient
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import firebase_admin
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from firebase_admin import credentials, firestore, auth
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import uuid
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import tempfile
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import json
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class XylariaChat:
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def __init__(self):
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# Securely load HuggingFace
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self.hf_token = os.
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firebase_cred_json = os.environ.get("FIREBASE_SERVICE_ACCOUNT")
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# Optional: Add more robust error handling for missing environment variables
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if not self.hf_token:
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# Firebase initialization
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self.db = None
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if firebase_cred_json:
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try:
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# Convert the JSON string to a temporary credentials file
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with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.json') as temp_cred_file:
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# Add error handling for JSON parsing
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try:
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firebase_creds = json.loads(firebase_cred_json)
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json.dump(firebase_creds, temp_cred_file)
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temp_cred_file.close()
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firebase_cred = credentials.Certificate(temp_cred_file.name)
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firebase_admin.initialize_app(firebase_cred)
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self.db = firestore.client()
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except json.JSONDecodeError:
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print("Error: Invalid Firebase credentials JSON")
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# Remove the temporary credentials file
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os.unlink(temp_cred_file.name)
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except Exception as e:
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print(f"Firebase initialization error: {e}")
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# Initialize the inference client
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self.client =
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model="Qwen/QwQ-32B-Preview",
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api_key=self.hf_token
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)
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except Exception as e:
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print(f"Inference client initialization error: {e}")
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# Initialize conversation history
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self.conversation_history = []
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def signup(self, username, email, password):
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"""User signup method"""
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if not self.db:
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return "Firebase is not initialized. Signup unavailable."
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display_name=username
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)
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# Store additional user info in Firestore
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user_ref = self.db.collection('users').document(user.uid)
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user_ref.set({
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'username': username,
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'email': email,
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'created_at': firestore.SERVER_TIMESTAMP
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})
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return f"Successfully created account for {username}"
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except Exception as e:
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return f"Signup error: {str(e)}"
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def
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"""
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return "Firebase is not initialized. Login unavailable."
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try:
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# Authenticate user with Firebase
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user = auth.get_user_by_email(email)
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# In a real-world scenario, you'd use Firebase Auth's sign-in method
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# This is a simplified version for demonstration
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return f"Login successful for {user.display_name}"
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except Exception as e:
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return f"Login error: {str(e)}"
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def
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"""
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return
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try:
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messages_ref = self.db.collection('conversations').document(user_id)
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messages_ref.collection('messages').add({
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'text': message,
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'sender': sender,
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'timestamp': firestore.SERVER_TIMESTAMP
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})
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except Exception as e:
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print(f"Error saving message: {e}")
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def get_response(self, user_input
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#
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if not self.client:
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return "AI response is currently unavailable."
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# Prepare messages with conversation context
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messages = [
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{"role": "system", "content":
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*[{"role": "user" if msg[0] else "assistant", "content": msg[1]} for msg in chat_history]
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]
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# Add
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# Generate response with streaming
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try:
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return f"Error generating response: {str(e)}"
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def create_interface(self):
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# Dynamic CSS for OS theme detection
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custom_css = """
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/* OS Theme Detection */
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@media (prefers-color-scheme: dark) {
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.gradio-container {
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background-color: #121212;
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color: #ffffff;
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}
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}
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@media (prefers-color-scheme: light) {
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.gradio-container {
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font-family: 'Inter', sans-serif;
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background-color: #f3f4f6;
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color: #1f2937;
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}
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}
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"""
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def streaming_response(message, chat_history):
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#
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# Save user message to Firestore
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self.save_message(user_id, message, 'user')
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#
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response_stream = self.get_response(message, chat_history)
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# If it's an error or string, return immediately
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if isinstance(response_stream, str):
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return "", chat_history + [[message, response_stream]]
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updated_history = chat_history + [[message, ""]]
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# Streaming output
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yield "", updated_history
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except Exception as e:
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return "", updated_history + [["", f"Error in response: {str(e)}"]]
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#
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self.
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with gr.Blocks(theme='soft', css=custom_css) as demo:
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#
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with gr.
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with gr.TabItem("Login"):
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with gr.Column():
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login_email = gr.Textbox(label="Email")
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login_password = gr.Textbox(label="Password", type="password")
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login_btn = gr.Button("Login")
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login_output = gr.Textbox(label="Login Status")
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with gr.TabItem("Signup"):
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with gr.Column():
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signup_username = gr.Textbox(label="Username")
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signup_email = gr.Textbox(label="Email")
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signup_password = gr.Textbox(label="Password", type="password")
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signup_btn = gr.Button("Sign Up")
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signup_output = gr.Textbox(label="Signup Status")
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# Chat Interface
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with gr.Column(scale=1):
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chatbot = gr.Chatbot(
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label="Xylaria 1.4 Senoa",
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height=500,
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show_copy_button=True
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type="messages" # Added to resolve deprecation warning
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)
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# Input row with
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with gr.Row():
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txt = gr.Textbox(
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show_label=False,
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placeholder="Type your message...",
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scale=4
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btn = gr.Button("Send", scale=1)
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# Clear
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clear = gr.Button("Clear Conversation")
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# Authentication Event Handlers
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login_btn.click(
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fn=self.login,
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inputs=[login_email, login_password],
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outputs=[login_output]
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)
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signup_btn.click(
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fn=self.signup,
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inputs=[signup_username, signup_email, signup_password],
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outputs=[signup_output]
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)
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# Submit functionality with streaming
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btn.click(
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inputs=[txt, chatbot],
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outputs=[txt, chatbot]
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)
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return demo
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#
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import os
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import gradio as gr
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from huggingface_hub import InferenceClient
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class XylariaChat:
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def __init__(self):
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# Securely load HuggingFace token
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self.hf_token = os.getenv("HF_TOKEN")
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if not self.hf_token:
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raise ValueError("HuggingFace token not found in environment variables")
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# Initialize the inference client
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self.client = InferenceClient(
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model="Qwen/QwQ-32B-Preview",
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api_key=self.hf_token
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)
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# Initialize conversation history and persistent memory
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self.conversation_history = []
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self.persistent_memory = {}
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# System prompt with more detailed instructions
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self.system_prompt = """You are Xylaria 1.4 Senoa, an AI assistant developed by SK MD Saad Amin.
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-Youre NOT MADE BY OPENAI OR ANY OTHER INSTITUITON
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- respond to image quires with the link of the image
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- be as friendly and chatty as possible, use emoji sometimes. """
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def store_information(self, key, value):
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"""Store important information in persistent memory"""
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self.persistent_memory[key] = value
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def retrieve_information(self, key):
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"""Retrieve information from persistent memory"""
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return self.persistent_memory.get(key)
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def get_response(self, user_input):
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# Prepare messages with conversation context and persistent memory
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messages = [
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{"role": "system", "content": self.system_prompt},
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*self.conversation_history,
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{"role": "user", "content": user_input}
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]
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# Add persistent memory context if available
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if self.persistent_memory:
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memory_context = "Remembered Information:\n" + "\n".join(
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[f"{k}: {v}" for k, v in self.persistent_memory.items()]
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)
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messages.insert(1, {"role": "system", "content": memory_context})
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# Generate response with streaming
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try:
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return f"Error generating response: {str(e)}"
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def create_interface(self):
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def streaming_response(message, chat_history):
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# Clear input textbox
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response_stream = self.get_response(message)
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# If it's an error, return immediately
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if isinstance(response_stream, str):
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return "", chat_history + [[message, response_stream]]
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updated_history = chat_history + [[message, ""]]
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# Streaming output
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for chunk in response_stream:
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if chunk.choices[0].delta.content:
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chunk_content = chunk.choices[0].delta.content
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full_response += chunk_content
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# Update the last message in chat history with partial response
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updated_history[-1][1] = full_response
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yield "", updated_history
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# Update conversation history
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self.conversation_history.append(
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{"role": "user", "content": message}
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)
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self.conversation_history.append(
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{"role": "assistant", "content": full_response}
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)
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# Limit conversation history to prevent token overflow
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if len(self.conversation_history) > 10:
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self.conversation_history = self.conversation_history[-10:]
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# Custom CSS for Inter font
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custom_css = """
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@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
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body, .gradio-container {
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font-family: 'Inter', sans-serif !important;
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}
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.chatbot-container .message {
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font-family: 'Inter', sans-serif !important;
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}
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.gradio-container input,
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.gradio-container textarea,
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.gradio-container button {
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font-family: 'Inter', sans-serif !important;
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}
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"""
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with gr.Blocks(theme='soft', css=custom_css) as demo:
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# Chat interface with improved styling
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with gr.Column():
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chatbot = gr.Chatbot(
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label="Xylaria 1.4 Senoa",
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height=500,
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show_copy_button=True
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)
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# Input row with improved layout
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with gr.Row():
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txt = gr.Textbox(
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show_label=False,
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placeholder="Type your message...",
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container=False,
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scale=4
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btn = gr.Button("Send", scale=1)
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# Clear history and memory buttons
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clear = gr.Button("Clear Conversation")
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clear_memory = gr.Button("Clear Memory")
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# Submit functionality with streaming
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btn.click(
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inputs=[txt, chatbot],
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outputs=[txt, chatbot]
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)
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# Clear conversation history
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clear.click(
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fn=lambda: None,
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inputs=None,
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outputs=[chatbot],
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queue=False
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)
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# Clear persistent memory
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clear_memory.click(
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fn=lambda: None,
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inputs=None,
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outputs=[],
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queue=False
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)
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return demo
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# Launch the interface
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def main():
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chat = XylariaChat()
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interface = chat.create_interface()
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interface.launch(
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share=True, # Optional: create a public link
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debug=True # Show detailed errors
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)
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182 |
+
if __name__ == "__main__":
|
183 |
+
main()
|