Upload folder using huggingface_hub

.env

@@ -0,0 +1,3 @@
+OPENAI_API_KEY=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
+MODEL_NAME=text-babbage-001
+PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+uploadeddocs/Duarte[[:space:]]Nancy[[:space:]]-[[:space:]]Slide_ology[[:space:]]the[[:space:]]art[[:space:]]and[[:space:]]science[[:space:]]of[[:space:]]creating[[:space:]]great[[:space:]]presentations[[:space:]]1.pdf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,6 @@
 ---
 title: DigitalTutor
-emoji: 📈
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.44.4
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.35.2
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,118 @@
+import os
+import PyPDF2
+import openai
+from llama_index import download_loader, set_global_service_context, VectorStoreIndex, LLMPredictor, PromptHelper, ServiceContext, StorageContext, load_index_from_storage
+from llama_index.readers import SimpleDirectoryReader
+from llama_index.tools import query_engine
+from langchain import OpenAI
+import gradio as gr
+from dotenv import load_dotenv
+import sys
+import shutil
+import pickle
+
+load_dotenv()
+
+#OPENAI_API_KEY=os.getenv("OPENAI_API_KEY")
+openai.api_key = os.environ["OPENAI_API_KEY"]
+MODEL_NAME=os.getenv("MODEL_NAME")
+docsdir = "uploadeddocs"
+indexdir = "uploadedindex"
+
+num_outputs = 512
+prompt_helper = PromptHelper(context_window = 3900, num_output = num_outputs, chunk_overlap_ratio= 0.1)
+llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name=MODEL_NAME, max_tokens=num_outputs))
+
+service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
+# set_global_service_context(service_context)
+
+
+def read_text_from_pdf(pdf_path):
+    with open(pdf_path, "rb") as file:
+        reader = PyPDF2.PdfReader(file)
+        text = ""
+        for page in range(len(reader.pages)):
+            text += reader.pages[page].extract_text()
+        return text
+
+def read_text_from_txt(txt_path):
+    with open(txt_path, "r") as file:
+        text = file.read()
+        return text
+
+def load_documents(directory_path):
+    documents = []
+    docmap = {}
+    for file_name in os.listdir(directory_path):
+        file_path = os.path.join(directory_path, file_name)
+        if file_name.endswith(".pdf"):
+            text = read_text_from_pdf(file_path)
+        elif file_name.endswith(".txt"):
+            text = read_text_from_txt(file_path)
+        else:
+            continue
+        #documents.append({"text": text, "file_name": file_name})
+        docmap["text"] = text
+    #return documents
+    return docmap
+
+def construct_index(directory_path):
+
+
+    storage_context = StorageContext.from_defaults()
+    documents = SimpleDirectoryReader(directory_path).load_data()
+    index = VectorStoreIndex.from_documents(documents, 
+					    llm_predictor=llm_predictor,
+					    prompt_helper=prompt_helper,
+                        storage_context=storage_context,
+                        service_context=service_context)
+    index.storage_context.persist(persist_dir=f'./uploadedindex')
+    # with open('./uploadedindex/index.pickle', 'wb') as handle:
+    #     pickle.dump(index, handle, protocol=pickle.HIGHEST_PROTOCOL)
+    
+    # index = load_index_from_storage(storage_context)
+    # query_engine = index.as_query_engine()
+    # return query_engine
+
+def load_index(directory_path):
+    storage_context = StorageContext.from_defaults(persist_dir=f'./uploadedindex')
+    index = load_index_from_storage(storage_context)
+    query_engine = index.as_query_engine()
+    return query_engine
+
+def process_upload(upload):
+    filename = upload.name
+    print(filename)
+    basename = os.path.basename(filename)
+    print(basename)
+    
+    for f in os.listdir(indexdir):
+        os.remove(os.path.join(indexdir, f))
+    for f in os.listdir(docsdir):
+        os.remove(os.path.join(docsdir, f))
+    shutil.copyfile(filename, docsdir + "/" + basename)
+    construct_index(docsdir)
+    text = "File Uploaded and Processed"
+    return text
+
+def chatbot(input_text):
+    storage_context = StorageContext.from_defaults(persist_dir=f'./uploadedindex')
+    index = load_index_from_storage(storage_context)
+    # with open('./uploadedindex/index.pickle', 'rb') as handle:
+    #     index = pickle.load(handle)
+    query_engine = index.as_query_engine()
+    # query = input_text + "explain in at least 100 words"
+    query = input_text + " if no nouns from the question is present in the book or have synonyms, reply that the question is not relevant to the book. if the question is relevant to the book, explain in at least 100 words in bullet points if possible"
+    response = query_engine.query(query)
+    return str(response)
+
+interface1 = gr.Interface(fn=process_upload, inputs=gr.inputs.File(label="Upload a PDF file", type="file"), outputs=gr.outputs.Textbox(label="PDF Text"), title="PDF File Upload")
+
+interface2 = gr.Interface(fn=chatbot,
+                    inputs=gr.inputs.Textbox(lines=7, label="Enter your text"),
+                    outputs="text",
+                    title="Ask Questions from the uploaded Book")
+
+iface = gr.TabbedInterface([interface1, interface2], ["Upload your book", "Ask a Question"])
+iface.launch(share=True)
+

uploadeddocs/Duarte Nancy - Slide_ology the art and science of creating great presentations 1.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2105921ae63ec254065d67ff69483f3ffdeed5422a6b77e937ec8cba62c12e56
+size 83974799

uploadedindex/graph_store.json

@@ -0,0 +1 @@
+{"graph_dict": {}}

uploadedindex/index_store.json

@@ -0,0 +1 @@
+{"index_store/data": {"cfe236c5-2188-46d4-ae2e-073faf932576": {"__type__": "vector_store", "__data__": "{\"index_id\": \"cfe236c5-2188-46d4-ae2e-073faf932576\", \"summary\": null, \"nodes_dict\": {\"dcae271b-585d-431b-9436-d2ee839c5986\": \"dcae271b-585d-431b-9436-d2ee839c5986\", \"20893bb5-5099-47ea-b0a9-7a7a7947b63f\": \"20893bb5-5099-47ea-b0a9-7a7a7947b63f\", \"dc8e794e-d2f3-4c32-9d57-ee762f642035\": \"dc8e794e-d2f3-4c32-9d57-ee762f642035\", \"71536a38-2719-4d73-b22c-4189c4d60f01\": \"71536a38-2719-4d73-b22c-4189c4d60f01\", \"87c34c18-554e-4e8a-be42-a9dfb09440cb\": \"87c34c18-554e-4e8a-be42-a9dfb09440cb\", \"3fbcff59-008d-41ec-9e3d-688262ebc9fe\": \"3fbcff59-008d-41ec-9e3d-688262ebc9fe\", \"68813999-4980-4af0-bd85-74d5eab95ddd\": \"68813999-4980-4af0-bd85-74d5eab95ddd\", \"3e756497-920d-438d-9244-3ed4e91d1264\": \"3e756497-920d-438d-9244-3ed4e91d1264\", \"bc9b5017-05a6-4ada-8296-c119e23d66db\": \"bc9b5017-05a6-4ada-8296-c119e23d66db\", \"3ec7cdff-a16a-4c78-a3c2-57516b466cc6\": 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