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from llama_index.llms.anthropic import Anthropic |
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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.embeddings.huggingface import HuggingFaceEmbedding |
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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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choices = ['open-mistral-7b', 'claude-3-haiku'] |
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def model_selection(choices): |
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if choices == "open-mistral-7b": |
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api_key = 'lJzlUC91kbvbMlOqCdAVorDdnmLEIU8b' |
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llm = MistralAI(api_key=api_key, model="open-mistral-7b") |
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embed_model = MistralAIEmbedding(model_name='mistral-embed', api_key=api_key) |
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else: |
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os.environ["ANTHROPIC_API_KEY"] = "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" |
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llm = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"], model = 'claude-3-haiku-20240307') |
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embed_model = HuggingFaceEmbedding(model_name='BAAI/bge-base-en-v1.5') |
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Settings.llm = llm |
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Settings.embed_model = embed_model |
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return choices |
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def qa(question: str, doc: str) -> str: |
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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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response = my_pdf_engine.query(question) |
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return response |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(): |
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model_choice = gr.Radio(choices=choices, label = 'Choose a model') |
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model_choice.change(model_selection, inputs=model_choice) |
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pdf_input = gr.File(label="Upload PDF") |
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with gr.Column(): |
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question_input = gr.Textbox(label="Ask Question from PDF document") |
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qa_button = gr.Button("Get Answer") |
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answer_output = gr.Textbox(label="Answer") |
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qa_button.click(fn=qa, inputs=[question_input, pdf_input], outputs=answer_output) |
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demo.launch(debug = True) |
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