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Browse files- main.py +111 -0
- requirements.txt +15 -0
main.py
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from langchain.llms import LlamaCpp
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from langchain.callbacks.manager import CallbackManager
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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import gradio as gr
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import re
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import os
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# MODEL_PATH = "persian_llama_7b.Q8_K_M.gguf"
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# TEMPRATURE = 0.3
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# MAX_TOKENS = 800
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# for k,v in os.environ.items():
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# if(k=="MODEL_PATH"):
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# MODEL_PATH = v
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# if(k== "TEMPRATURE"):
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# TEMPRATURE = v
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# if(k == "MAX_TOKENS"):
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# MAX_TOKENS = v
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# print("model: "+MODEL_PATH)
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# print("temp: "+TEMPRATURE)
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# print("max_tokens: "+MAX_TOKENS)
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n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool.
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n_batch = 512 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
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n_ctx=2048
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callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
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path = "mostafaamiri/persian-llama-7b-GGUF-Q4/persian_llama_7b.Q8_K_M.gguf"
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# persian_llama_7b.Q4_K_M.gguf
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# persian_llama_7b.Q8_K_M.gguf
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# persian_llama_7b.f32.gguf
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# Make sure the model path is correct for your system!
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llm = LlamaCpp(
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model_path= path,
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n_gpu_layers=n_gpu_layers, n_batch=n_batch,
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callback_manager=callback_manager,
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verbose=True,
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n_ctx=n_ctx,
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temperature=TEMPRATURE,
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max_tokens=MAX_TOKENS,
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top_p=1,
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)
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dal_image = ""
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def generate_output(text):
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result = ""
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for s in llm.stream(text):
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result += s
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yield result
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def clear():
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return "", ""
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def like_log(input, output):
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with open("like_log.txt", "a") as f:
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f.write("{\"model\": \""+MODEL_PATH+"\",\n\"temprature\": "+TEMPRATURE+",\n\"input\": \""+input+"\",\n\"output\": \""+output+"\"},\n")
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def dislike_log(input, output):
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with open("dislike_log.txt", "a") as f:
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f.write("{\"model\": \""+MODEL_PATH+"\",\n\"temprature\": "+TEMPRATURE+",\n\"input\": \""+input+"\",\n\"output\": \""+output+"\"},\n")
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown(
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dal_image+
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"""
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<br>
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<div dir="rtl">
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<h1>
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مدل هوش مصنوعی دال
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</h1>
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<p dir="rtl">
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تماس با ما با
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<br/>
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info[@]aidal.ir
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</p>
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</div>
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""")
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with gr.Row():
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inputs=gr.Textbox(label="ورودی",placeholder="سوال خود را وارد کنید",rtl=True)
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with gr.Row():
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submit_btn= gr.Button("ارسال", variant="primary")
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clear_btn = gr.ClearButton(value="پاک کردن", variant="secondary")
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with gr.Row():
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outputs=gr.Textbox(label="خروجی",rtl=True)
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submit_btn.click(fn=generate_output,
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inputs= [inputs],
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outputs= [outputs])
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clear_btn.click(fn=clear, inputs=[], outputs=[inputs, outputs])
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with gr.Row():
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like_btn= gr.Button("👍🏾")
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dislike_btn= gr.Button("👎🏾")
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like_btn.click(fn=like_log,
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inputs= [inputs, outputs],
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outputs=[]
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)
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dislike_btn.click(fn=dislike_log,
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inputs= [inputs, outputs],
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outputs=[]
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)
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# gr_interface = gr.Interface(fn=generate_output,
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# inputs=gr.Textbox(label="ورودی",placeholder="سوال خود را وارد کنید",rtl=True),
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# outputs=gr.Textbox(label="خروجی",rtl=True),
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# live=False,
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# flagging_options=["👍🏾","👎🏾"],
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# concurrency_limit=5)
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demo.launch(server_name='0.0.0.0',share=True)
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requirements.txt
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langchain==0.0.335
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langsmith==0.0.64
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peft==0.7.0
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safetensors==0.3.1
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scikit-learn==1.3.0
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scipy==1.11.1
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sentencepiece==0.1.99
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tokenizers==0.15.0
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torch==2.0.1
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torchaudio==2.0.2
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torchvision==0.15.2
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transformers==4.36.0
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bitsandbytes==0.41.1
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gradio==4.13.0
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llama-cpp-python==0.2.28
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