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Update app.py
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app.py
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import gradio as gr
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import os
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from huggingface_hub import InferenceClient
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import time
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hf_token = os.getenv("hf_token")
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response = ""
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for chunk in stream:
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response += chunk.choices[0].delta.content
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time.sleep(0.05) # Optional: Adjust speed of the stream (in seconds)
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def chat_interface():
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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chat_output = gr.Chatbot(
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elem_id="chat-box",
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label="Xylaria 1.4 Senoa Chatbot",
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show_label=False,
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type="messages" # Specify type for correct message format
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)
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with gr.Row(elem_id="input-row"):
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with gr.Column(): # No 'min_width' argument here
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input_textbox = gr.Textbox(
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label="Type your message",
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placeholder="Ask me anything...",
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elem_id="user-input",
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show_label=False
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)
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with gr.Column():
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send_button = gr.Button("Send", elem_id="send-btn")
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def submit_input(user_input, chat_history):
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input_textbox.submit(submit_input, [input_textbox, chat_output], [input_textbox, chat_output])
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send_button.click(submit_input, [input_textbox, chat_output], [input_textbox, chat_output])
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def handle_response(user_input, chat_history):
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# Initialize chat_history if it's empty
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if not chat_history:
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chat_history = [{"role": "system", "content": "you are xylaria 1.4 senoa, developed by sk md saad amin"}]
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# Add user input
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chat_history.append({"role": "user", "content": user_input})
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# Get response from model
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response_stream = get_response(user_input)
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for partial_response in response_stream:
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chat_history[-1] = {"role": "assistant", "content": partial_response}
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yield "", chat_history # Return the updated chat history progressively
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input_textbox.submit(handle_response, [input_textbox, chat_output], [input_textbox, chat_output])
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send_button.click(handle_response, [input_textbox, chat_output], [input_textbox, chat_output])
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demo.css = """
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#input-row {
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position: absolute;
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bottom: 10px;
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width: 100%;
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padding: 10px;
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background-color: #f5f5f5;
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border-top: 1px solid #ddd;
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}
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#chat-box {
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height: calc(100vh - 100px); /* Adjust the height of chat history */
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overflow-y: scroll;
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}
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"""
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return demo
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demo = chat_interface()
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import gradio as gr
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import os
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from huggingface_hub import InferenceClient
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hf_token = os.getenv("hf_token")
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response = ""
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for chunk in stream:
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response += chunk.choices[0].delta.content
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return response
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def chat_interface():
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column(scale=0.8):
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input_textbox = gr.Textbox(
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label="Type your message",
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placeholder="Ask me anything...",
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elem_id="user-input",
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show_label=False
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)
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with gr.Column(scale=0.2):
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send_button = gr.Button("Send", elem_id="send-btn")
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chat_output = gr.Chatbot(
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elem_id="chat-box",
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label="Xylaria 1.4 Senoa Chatbot",
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show_label=False
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)
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def submit_input(user_input, chat_history):
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response = get_response(user_input)
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chat_history.append((user_input, response))
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return "", chat_history
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input_textbox.submit(submit_input, [input_textbox, chat_output], [input_textbox, chat_output])
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send_button.click(submit_input, [input_textbox, chat_output], [input_textbox, chat_output])
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return demo
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demo = chat_interface()
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