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from llama_index.llms.mistralai import MistralAI |
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from llama_index.embeddings.mistralai import MistralAIEmbedding |
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from llama_index.core.settings import Settings |
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from llama_index.core import SimpleDirectoryReader, VectorStoreIndex |
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import gradio as gr |
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from gradio_pdf import PDF |
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import os |
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api_key = 'Of59Qz8Enr4fVj11XoKLRkNHENULLpLt' |
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my_list=['open-mistral-7b', 'open-mixtral-8x7b', 'mistral-small-latest','mistral-medium-latest','mistral-large-latest'] |
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mdel= my_list[3] |
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llm = MistralAI(api_key=api_key, model=mdel) |
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embed_model = MistralAIEmbedding(model_name='mistral-embed', api_key=api_key) |
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Settings.llm = llm |
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Settings.embed_model = embed_model |
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def qa(model: str, question: str, doc: str, mdel: str) -> str: |
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if mdel != model: |
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mdel= model |
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llm = MistralAI(api_key=api_key, model=mdel) |
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my_pdf = SimpleDirectoryReader(input_files=[doc]).load_data() |
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my_pdf_index = VectorStoreIndex.from_documents(my_pdf) |
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my_pdf_engine = my_pdf_index.as_query_engine() |
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question = "tu n'utile pas la langue anglaises, tu reponds en francais, " + question |
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response = my_pdf_engine.query(question) |
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return response |
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demo = gr.Interface( |
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qa, |
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[ gr.Dropdown(choices=my_list, label="model",value=mdel), |
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gr.Textbox(label="Question"), PDF(label="Document")], |
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gr.Textbox()) |
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if __name__ == "__main__": |
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demo.launch(auth=("username", "password")) |
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