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NithyasriVllB
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Update app.py
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app.py
CHANGED
@@ -1,57 +1,57 @@
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import gradio as gr
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from huggingface_hub import InferenceClient
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# Function to return the appropriate client based on the model selected
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def client_fn(model):
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model_map = {
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"Nous Hermes Mixtral 8x7B DPO": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
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"StarChat2 15b": "HuggingFaceH4/starchat2-15b-v0.1",
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"Mistral 7B v0.3": "mistralai/Mistral-7B-Instruct-v0.3",
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"Phi 3 mini": "microsoft/Phi-3-mini-4k-instruct",
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"Mixtral 8x7B": "mistralai/Mixtral-8x7B-Instruct-v0.1"
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}
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return InferenceClient(model_map.get(model, "mistralai/Mixtral-8x7B-Instruct-v0.1"))
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system_instructions = ("[SYSTEM] You are a chat bot named '
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"Your task is to Answer the question."
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"Keep conversation very short, clear and concise."
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"Respond naturally and concisely to the user's queries. "
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"The expectation is that you will avoid introductions and start answering the query directly, Only answer the question asked by user, Do not say unnecessary things."
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"Begin with a greeting if the user initiates the conversation. "
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"Here is the user's query:[QUESTION] ")
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# Function to generate model responses
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def models(text, model="Mixtral 8x7B"):
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client = client_fn(model)
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generate_kwargs = {
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"max_new_tokens": 100,
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"do_sample": True,
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}
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formatted_prompt = f"{system_instructions} {text} [ANSWER]"
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stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
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output = ""
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for response in stream:
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output += response.token.text
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if output.endswith("</s>"):
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output = output[:-4]
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return output
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# Gradio interface description and configuration
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description = """# H GO
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### Inspired from Google Go"""
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with gr.Blocks() as demo:
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gr.Markdown(description)
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text_input = gr.Textbox(label="Enter your message here:")
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dropdown = gr.Dropdown(['Mixtral 8x7B', 'Nous Hermes Mixtral 8x7B DPO', 'StarChat2 15b', 'Mistral 7B v0.3', 'Phi 3 mini'], value="Mistral 7B v0.3", label="Select Model")
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submit_btn = gr.Button("Send")
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output_text = gr.Textbox(label="Response")
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submit_btn.click(fn=models, inputs=[text_input, dropdown], outputs=output_text)
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# Queue and launch configuration for Gradio
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demo.queue(max_size=300000)
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demo.launch()
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import gradio as gr
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from huggingface_hub import InferenceClient
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# Function to return the appropriate client based on the model selected
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def client_fn(model):
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model_map = {
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"Nous Hermes Mixtral 8x7B DPO": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
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"StarChat2 15b": "HuggingFaceH4/starchat2-15b-v0.1",
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"Mistral 7B v0.3": "mistralai/Mistral-7B-Instruct-v0.3",
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"Phi 3 mini": "microsoft/Phi-3-mini-4k-instruct",
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"Mixtral 8x7B": "mistralai/Mixtral-8x7B-Instruct-v0.1"
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}
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return InferenceClient(model_map.get(model, "mistralai/Mixtral-8x7B-Instruct-v0.1"))
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system_instructions = ("[SYSTEM] You are a chat bot named 'NITHIYASRI'S CHATBOT'."
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"Your task is to Answer the question."
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"Keep conversation very short, clear and concise."
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"Respond naturally and concisely to the user's queries. "
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"The expectation is that you will avoid introductions and start answering the query directly, Only answer the question asked by user, Do not say unnecessary things."
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"Begin with a greeting if the user initiates the conversation. "
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"Here is the user's query:[QUESTION] ")
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# Function to generate model responses
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def models(text, model="Mixtral 8x7B"):
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client = client_fn(model)
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generate_kwargs = {
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"max_new_tokens": 100,
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"do_sample": True,
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}
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formatted_prompt = f"{system_instructions} {text} [ANSWER]"
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stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
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output = ""
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for response in stream:
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output += response.token.text
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if output.endswith("</s>"):
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output = output[:-4]
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return output
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# Gradio interface description and configuration
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description = """# H GO
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### Inspired from Google Go"""
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with gr.Blocks() as demo:
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gr.Markdown(description)
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text_input = gr.Textbox(label="Enter your message here:")
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dropdown = gr.Dropdown(['Mixtral 8x7B', 'Nous Hermes Mixtral 8x7B DPO', 'StarChat2 15b', 'Mistral 7B v0.3', 'Phi 3 mini'], value="Mistral 7B v0.3", label="Select Model")
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submit_btn = gr.Button("Send")
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output_text = gr.Textbox(label="Response")
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submit_btn.click(fn=models, inputs=[text_input, dropdown], outputs=output_text)
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# Queue and launch configuration for Gradio
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demo.queue(max_size=300000)
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demo.launch()
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