Upload folder using huggingface_hub

.gitattributes

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+ece120-spring-2022-notes-for-students-1.pdf filter=lfs diff=lfs merge=lfs -text
+textbook.pdf filter=lfs diff=lfs merge=lfs -text

.gitignore

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+venv/
+venv2/
+.env
+__pycache__/chroma_db_textbook_c/chroma.sqlite3
+ai-ta-env/lib/python3.12/site-packages/onnxruntime/capi/onnxruntime_pybind11_state.so
+chroma_db_textbook_c/chroma.sqlite3
+ai-ta-env/lib/python3.12/site-packages/torch/lib/libtorch_cpu.dylib
+ai-ta-env
+echo "/Users/nittinmurthi/Documents/VS_Code/120-AI-TA/course-logistics-retriever" >> .gitignore
+echo "/Users/nittinmurthi/Documents/VS_Code/120-AI-TA/ai-ta-env" >> .gitignore

.gradio/certificate.pem

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README.md

@@ -1,13 +1,7 @@
----
-title: 120 AI TA
-emoji: 🦀
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.0.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: 120-AI-TA
+app_file: app.py
+sdk: gradio
+sdk_version: 5.0.1
+---
+# 120-AI-Assistant

README_utf8.md

@@ -0,0 +1 @@
+# 120-AI-Assistant

app.py

@@ -0,0 +1,7 @@
+import gradio as gr
+
+from gradio_demo import build_app
+
+if __name__ == "__main__":
+    app = build_app()
+    app.queue().launch(server_name="0.0.0.0", server_port=4700, share=True)

build_db.py

@@ -0,0 +1,195 @@
+import tiktoken
+import json
+import glob
+import os
+# import latex2markdown
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.document_loaders import PyPDFLoader
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.docstore.document import Document
+from langchain.document_loaders import UnstructuredHTMLLoader
+from langchain.document_loaders import BSHTMLLoader
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+model_name = "gpt-3.5-turbo-16k"
+
+encoding = tiktoken.encoding_for_model(model_name)
+
+
+def get_tiktoken_length(text):
+    return len(encoding.encode(text))
+
+# Build syllabus DB
+
+
+loader = TextLoader('course_name.txt')
+raw_documents = loader.load()
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=512,
+    chunk_overlap=128,
+    length_function=get_tiktoken_length,
+    is_separator_regex=False,
+)
+
+documents = text_splitter.split_documents(raw_documents)
+
+embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
+
+db = Chroma.from_documents(
+    documents, embedding=embedding, persist_directory="chroma_db_course_name"
+)
+
+
+# # Build testbook DB
+
+loader = PyPDFLoader("textbook.pdf")
+pages = loader.load_and_split()
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=512,
+    chunk_overlap=128,
+    length_function=get_tiktoken_length,
+    is_separator_regex=False,
+)
+
+documents = text_splitter.split_documents(pages)
+
+embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
+
+db = Chroma.from_documents(
+    documents, embedding=embedding, persist_directory="chroma_db_textbook"
+)
+
+
+# Build course overview
+
+loader = PyPDFLoader("course_overview.pdf")
+pages = loader.load_and_split()
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=512,
+    chunk_overlap=128,
+    length_function=get_tiktoken_length,
+    is_separator_regex=False,
+)
+
+documents = text_splitter.split_documents(pages)
+
+embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
+
+db = Chroma.from_documents(
+    documents, embedding=embedding, persist_directory="chroma_db_course_overview"
+)
+
+# build Overview from html
+# loader = BSHTMLLoader(
+#     "Course Overview and Policies - Fall 2023_ Fall 2023-ECE 120-Introduction to Computing-Sections AD1, AD2, AD3, AD4, AD5, AD6, AD7, AD8, AD9, ADA, ADB, ADC, AL1, AL2, AL3, AL5.html")
+# data = loader.load()
+
+# text_splitter = RecursiveCharacterTextSplitter(
+#     chunk_size=512,
+#     chunk_overlap=128,
+#     length_function=get_tiktoken_length,
+#     is_separator_regex=False,
+# )
+
+# documents = text_splitter.split_documents(data)
+
+# embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
+
+# print(documents)
+# db = Chroma.from_documents(
+#     documents, embedding=embedding, persist_directory="chroma_db_courseoverview_policies"
+# )
+
+
+# build Office Hours from html
+# loader = BSHTMLLoader(
+#     "Course Overview and Policies - Fall 2023 Fall 2023-ECE 120-Introduction to Computing.html")
+# data = loader.load()
+
+# text_splitter = RecursiveCharacterTextSplitter(
+#     chunk_size=512,
+#     chunk_overlap=128,
+#     length_function=get_tiktoken_length,
+#     is_separator_regex=False,
+# )
+
+# documents = text_splitter.split_documents(data)
+
+# embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
+
+# print(documents)
+# db = Chroma.from_documents(
+#     documents, embedding=embedding, persist_directory="chroma_db_courseoverview_policies"
+# )
+
+# Build notebook DB
+
+loader = PyPDFLoader("ece120-spring-2022-notes-for-students-1.pdf")
+pages = loader.load_and_split()
+# print(pages[0])
+
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=512,
+    chunk_overlap=128,
+    length_function=get_tiktoken_length,
+    is_separator_regex=False,
+)
+
+documents = text_splitter.split_documents(pages)
+
+embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
+
+db = Chroma.from_documents(
+    documents, embedding=embedding, persist_directory="chroma_db_notes"
+)
+
+
+# Build from canvas
+
+# directory_path = "./canvas"
+
+# # Loop through all files in the directory
+# for filename in os.listdir(directory_path):
+#     file_path = os.path.join(directory_path, filename)
+#     if os.path.isfile(file_path) and filename != ".DS_Store":
+
+#         # print(filename)
+#         if filename.endswith(".html"):
+#             loader = UnstructuredHTMLLoader(file_path)
+#             data = loader.load()
+#         else:
+#             loader = PyPDFLoader(file_path)
+#             data = loader.load_and_split()
+
+#         # if (filename == "Office Hours FA23.html"):
+#         #     #print(data)
+#         # if (filename.endswith(".pdf")):
+#         #     print(data)
+#         text_splitter = RecursiveCharacterTextSplitter(
+#             chunk_size=512,
+#             chunk_overlap=128,
+#             length_function=get_tiktoken_length,
+#             is_separator_regex=False,
+#         )
+
+#         documents = text_splitter.split_documents(data)
+
+#         embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
+
+#         # print(documents)
+#         filename = filename.replace(".html", "")
+#         filename = filename.replace(".pdf", "")
+#         filename = filename.replace(" ", "_")
+#         db_name = "chroma_db_" + filename
+#         print(db_name)
+#         db = Chroma.from_documents(
+#             documents, embedding=embedding, persist_directory=db_name
+#         )

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