commit d3d977a3d053c11ac5e964b7e88ae9c702b5257b
Author: Romain Quinet <r.quinet@evs.com>
Date:   Fri Oct 6 21:35:53 2023 +0200

    initial commit

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..b66076d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+/data
+/index
+/.idea
\ No newline at end of file
diff --git a/download_transcripts.py b/download_transcripts.py
new file mode 100644
index 0000000..5b9f382
--- /dev/null
+++ b/download_transcripts.py
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+import requests
+from bs4 import BeautifulSoup
+
+for i in range(1, 139):
+    url = f"https://darknetdiaries.com/transcript/{i}"
+    r = requests.get(url)
+    soup = BeautifulSoup(r.text, 'html.parser')
+    pre_section = soup.find('pre')
+
+    if pre_section:
+        text = pre_section.get_text()
+        with open(f"data/episode_{i}.txt", "w") as f:
+            f.write(text)
\ No newline at end of file
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..3afda1a
--- /dev/null
+++ b/main.py
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+from llama_index import (SimpleDirectoryReader, ServiceContext, StorageContext, PromptTemplate,
+                         load_index_from_storage, Document, set_global_service_context)
+from llama_index.node_parser import SimpleNodeParser
+from llama_index import VectorStoreIndex
+from llama_index.llms import OpenAI
+import os
+import re
+
+llm = OpenAI(model="gpt-3.5-turbo", temperature=0, max_tokens=256)
+service_context = ServiceContext.from_defaults(llm=llm)
+set_global_service_context(service_context)
+
+if not os.path.exists("./index/lock"):
+    documents = []
+    for filename in os.listdir("./data"):
+        episode_number = re.search(r'\d+', filename).group()
+        with open("./data/" + filename, 'r') as f:
+            content = f.read()
+        document = Document(
+            text=content,
+            metadata={
+                "episode_number": episode_number
+            }
+        )
+
+    documents = SimpleDirectoryReader('./data').load_data()
+    parser = SimpleNodeParser.from_defaults()
+    nodes = parser.get_nodes_from_documents(documents)
+
+    index = VectorStoreIndex(nodes, show_progress=True)
+    index.storage_context.persist(persist_dir="./index")
+    open("./index/lock", 'a').close()
+else:
+    storage_context = StorageContext.from_defaults(persist_dir="./index")
+    index = load_index_from_storage(storage_context)
+
+template = (
+    "You are now an expert on the Darknet Diaries podcast. \n"
+    "Please answer this question by referring to the podcast: {query_str}\n"
+)
+qa_template = PromptTemplate(template)
+query_engine = index.as_query_engine(text_qa_template=qa_template)
+
+while True:
+    try:
+        user_prompt = input("Prompt: ")
+        response = query_engine.query(user_prompt)
+        print(response)
+    except KeyboardInterrupt:
+        break
+