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Browse files- .gitattributes +3 -0
- app.py +40 -0
- trainingData/.DS_Store +0 -0
- trainingData/Endangered Species Act.pdf +0 -0
- trainingData/Environment Protection Act.pdf +3 -0
- trainingData/Highway Traffic Act.pdf +3 -0
- trainingData/Legislation Act.pdf +0 -0
- trainingData/Nutrient Management Act.pdf +0 -0
- trainingData/Pesticides Act.pdf +0 -0
- trainingData/Safe Drinking Water Act.pdf +0 -0
- trainingData/Water Resource Act.pdf +3 -0
.gitattributes
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@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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trainingData/Environment[[:space:]]Protection[[:space:]]Act.pdf filter=lfs diff=lfs merge=lfs -text
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trainingData/Highway[[:space:]]Traffic[[:space:]]Act.pdf filter=lfs diff=lfs merge=lfs -text
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trainingData/Water[[:space:]]Resource[[:space:]]Act.pdf filter=lfs diff=lfs merge=lfs -text
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app.py
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from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, LLMPredictor, ServiceContext, StorageContext, load_index_from_storage
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from langchain import OpenAI
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import gradio
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import os
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os.environ["OPENAI_API_KEY"] = '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'
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def construct_index(directory_path):
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num_outputs = 512
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_llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=num_outputs))
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service_context = ServiceContext.from_defaults(llm_predictor=_llm_predictor)
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docs = SimpleDirectoryReader(directory_path).load_data()
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index = GPTVectorStoreIndex.from_documents(docs, service_context=service_context)
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index.storage_context.persist(persist_dir="indexes")
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return index
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def chatbot(input_text):
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storage_context = StorageContext.from_defaults(persist_dir="indexes")
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query_engne = load_index_from_storage(storage_context).as_query_engine()
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response = query_engne.query(input_text)
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return response.response
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iface = gradio.Interface(fn=chatbot,
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inputs=gradio.inputs.Textbox(lines=4, label="Enter your question here"),
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outputs=gradio.outputs.Textbox(label="Generated Text"),
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title="My Custom trained AI Chatbot")
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index = construct_index("trainingData")
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iface.launch()
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trainingData/.DS_Store
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Binary file (6.15 kB). View file
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trainingData/Endangered Species Act.pdf
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Binary file (474 kB). View file
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trainingData/Environment Protection Act.pdf
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version https://git-lfs.github.com/spec/v1
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oid sha256:a035f8bc013734aeff419ea2579d6fb2b4916e49359a2715241948448096d0aa
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size 1557709
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trainingData/Highway Traffic Act.pdf
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version https://git-lfs.github.com/spec/v1
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oid sha256:133d4cf0c34f5f51c7a61bd4c0838bcccf10e5b514772e5083876e95089f2e0d
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size 2533591
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trainingData/Legislation Act.pdf
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Binary file (345 kB). View file
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trainingData/Nutrient Management Act.pdf
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Binary file (397 kB). View file
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trainingData/Pesticides Act.pdf
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Binary file (508 kB). View file
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trainingData/Safe Drinking Water Act.pdf
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Binary file (798 kB). View file
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trainingData/Water Resource Act.pdf
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version https://git-lfs.github.com/spec/v1
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oid sha256:20be8b2476df8ca3e2eb97568d1c2bba9c9dd2275d126234e10f6f83827761a2
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size 1088838
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