Spaces:
Running
Running
Update in Gradio
Browse files
app.py
CHANGED
@@ -10,7 +10,7 @@ client = InferenceClient(model=HF_MODEL_NAME, token=HF_API_KEY)
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def respond(
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message,
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history: list[
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system_message,
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customer_profile,
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customer_goals,
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@@ -21,7 +21,6 @@ def respond(
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temperature,
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top_p,
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):
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# Construct the system message with additional inputs
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enhanced_system_message = (
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f"{system_message}\n\n"
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f"Customer Profile: {customer_profile}\n"
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@@ -30,23 +29,13 @@ def respond(
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f"Main Topic: {main_topic}\n"
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)
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# If the user wants topic suggestions, modify the prompt
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if ask_for_topic_suggestions:
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enhanced_system_message += "
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messages = [{"role": "system", "content": enhanced_system_message}]
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-
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# Add conversation history
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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# Add the current user message
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messages.append({"role": "user", "content": message})
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# Generate the response
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response = ""
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for message in client.chat_completion(
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messages,
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@@ -60,7 +49,9 @@ def respond(
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yield response
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# Define the Gradio interface
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demo = gr.ChatInterface(
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respond,
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additional_inputs=[
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@@ -91,6 +82,7 @@ demo = gr.ChatInterface(
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],
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title="SEO Assistant",
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description="This app provides customized content that resonates with your customers to improve your SEO. Based on your input. Powered by Hugging Face Inference, Design Thinking, and domain expertise. Expand Additional Inputs by clicking on the arrow, input more details about your customers, then a message describing what you need the assistant to do for you. Developed by wn. Disclaimer: AI can make mistakes. Use with caution and at your own risk!",
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)
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def respond(
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message,
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history: list[dict],
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system_message,
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customer_profile,
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customer_goals,
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temperature,
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top_p,
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):
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enhanced_system_message = (
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f"{system_message}\n\n"
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f"Customer Profile: {customer_profile}\n"
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f"Main Topic: {main_topic}\n"
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)
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if ask_for_topic_suggestions:
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enhanced_system_message += "\nThe user is also asking for topic suggestions to address their customer's needs."
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messages = [{"role": "system", "content": enhanced_system_message}]
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messages.extend(history)
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messages.append({"role": "user", "content": message})
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response = ""
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for message in client.chat_completion(
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messages,
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yield response
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# Define the Gradio interface
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demo = gr.ChatInterface(
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respond,
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additional_inputs=[
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],
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title="SEO Assistant",
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description="This app provides customized content that resonates with your customers to improve your SEO. Based on your input. Powered by Hugging Face Inference, Design Thinking, and domain expertise. Expand Additional Inputs by clicking on the arrow, input more details about your customers, then a message describing what you need the assistant to do for you. Developed by wn. Disclaimer: AI can make mistakes. Use with caution and at your own risk!",
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type="messages", # <-- Add this line
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)
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