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text2text/main.py
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text2text/main.py
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from ollama import Client
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model = 'llama3.2'
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prompt = "Wieso ist der Himmel blau?"
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top_k = 40
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top_p = 0.9
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temp=0.8
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client = Client(host='http://172.22.0.29:11434')
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response = client.generate(
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model=model,
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prompt= prompt,
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stream= False,
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options= {
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'top_k': top_k,
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'temperature':top_p,
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'top_p': temp
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}
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)
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print(response.response)
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text2text/main_gradio.py
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text2text/main_gradio.py
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from ollama import Client
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import json
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import gradio as gr
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model = 'llama3.2'
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context = []
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client = Client(
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host='http://172.22.0.29:11434',
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)
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def generate(prompt, context, top_k, top_p, temp):
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global client
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response = client.generate(
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model=model,
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prompt= prompt,
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context= context,
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stream= False,
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options= {
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'top_k': top_k,
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'temperature':top_p,
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'top_p': temp
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}
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)
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return response.response, response.context
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def chat(input, chat_history, top_k, top_p, temp):
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chat_history = chat_history or []
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global context
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output, context = generate(input, context, top_k, top_p, temp)
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chat_history.append((input, output))
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return chat_history, chat_history
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#########################Gradio Code##########################
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block = gr.Blocks()
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with block:
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gr.Markdown("""<h1><center> Mein Assistent </center></h1>""")
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chatbot = gr.Chatbot()
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message = gr.Textbox(placeholder="Type here")
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state = gr.State()
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with gr.Row():
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top_k = gr.Slider(0.0,100.0, label="top_k", value=40, info="Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)")
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top_p = gr.Slider(0.0,1.0, label="top_p", value=0.9, info=" Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)")
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temp = gr.Slider(0.0,2.0, label="temperature", value=0.8, info="The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)")
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submit = gr.Button("Send")
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submit.click(chat, inputs=[message, state, top_k, top_p, temp], outputs=[chatbot, state])
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block.launch(debug=True)
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