Update openai key

app.py

@@ -18,7 +18,7 @@ import random
 import time
 import together
 
-os.environ['OPENAI_API_KEY']='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'
+os.environ['OPENAI_API_KEY']='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'
 os.environ['ACTIVELOOP_TOKEN']='eyJhbGciOiJIUzUxMiIsImlhdCI6MTY4MTU5NTgyOCwiZXhwIjoxNzEzMjE4MTU5fQ.eyJpZCI6ImFpc3dhcnlhcyJ9.eoiMFZsS20zzMXXupFbowUlLdgIgf_MA1ck_DByzREeoQvNm8GPhKEfqea2y1Qak-ud2jo9dhSTBTfRe1ztezw'
 
 
@@ -119,10 +119,11 @@ def index_repo(textbox: str, dropdown: str) -> Response:
     print("Repo name after setting the value: " + str(repoName))
     pathName = git_clone(repo)
     root_dir = './' + pathName
+    print(root_dir)
 
     print("Repo name after setting the value: " + str(repoName))
     activeloop_username = "aiswaryas"
-    dataset_path = f"hub://{activeloop_username}/" + pathName + "1000"
+    dataset_path = f"hub://{activeloop_username}/" + pathName + "3"
     print(dataset_path)
 
     try:
@@ -153,7 +154,7 @@ def index_repo(textbox: str, dropdown: str) -> Response:
                             pass
 
                 activeloop_username = "aiswaryas"
-                dataset_path = f"hub://{activeloop_username}/" + pathName + "1000"
+                dataset_path = f"hub://{activeloop_username}/" + pathName + "3"
                 text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
                 texts = text_splitter.split_documents(docs)
 
@@ -194,10 +195,8 @@ def index_repo(textbox: str, dropdown: str) -> Response:
         )
 
     global ticket_choices, ticket_titles, tickets
-    print("REPO name in bug triage: " + str(repoName))
     repo = "/".join(repoName[:-4].split("/")[-2:])
     tickets = fetchGithubIssues(repo, 10)
-    # print("tickets: " + str(tickets))
 
     # Create the dropdown
     ticket_choices = {ticket["title"]: ticket for ticket in tickets}
@@ -218,9 +217,9 @@ def answer_questions(question: str, github: str, **kwargs) -> Response:
     github = repoName[:-4]
     print(github)
     try:
-        embeddings = OpenAIEmbeddings(openai_api_key="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")
+        embeddings = OpenAIEmbeddings(openai_api_key="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")
         pathName = github.split('/')[-1]
-        dataset_path = "hub://aiswaryas/" + pathName + "1000"
+        dataset_path = "hub://aiswaryas/" + pathName + "3"
 
         db = DeepLake(dataset_path=dataset_path, read_only=True, embedding_function=embeddings)
 
@@ -241,7 +240,7 @@ def answer_questions(question: str, github: str, **kwargs) -> Response:
             callback_manager=CallbackManager(
                 [StreamingGradioCallbackHandler(q)]
             ),
-            openai_api_key="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",
+            openai_api_key="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",
         )
         qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)
         chat_history = []
@@ -306,7 +305,7 @@ def generateFolderNamesForRepo(repo):
         input data and generate the responses that are displayed in the UI.
     """
     pathName = git_clone(repo)
-    root_dir = './' + pathName + "1000"
+    root_dir = './' + pathName + "3"
 
     files, dirs, docs = [], [], []
     for dirpath, dirnames, filenames in os.walk(root_dir):
@@ -342,10 +341,10 @@ def generateDocumentationPerFolder(dir, github):
 
     print(prompt)
     try:
-        embeddings = OpenAIEmbeddings(openai_api_key="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")
+        embeddings = OpenAIEmbeddings(openai_api_key="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")
         pathName = github.split('/')[-1]
         print("PATH NAME: " + str(pathName))
-        dataset_path = "hub://aiswaryas/" + pathName + "1000"
+        dataset_path = "hub://aiswaryas/" + pathName + "3"
 
         db = DeepLake(dataset_path=dataset_path, read_only=True, embedding_function=embeddings)
 
@@ -362,7 +361,7 @@ def generateDocumentationPerFolder(dir, github):
             temperature=0.0,
             verbose=True,
             streaming=True,  # Pass `streaming=True` to make sure the client receives the data.
-            openai_api_key="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",
+            openai_api_key="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",
         )
         qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)
         chat_history = []
@@ -405,9 +404,9 @@ def solveGithubIssue(ticket, history) -> Response:
     print(question)
 
     try:
-        embeddings = OpenAIEmbeddings(openai_api_key="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")
+        embeddings = OpenAIEmbeddings(openai_api_key="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")
         pathName = github.split('/')[-1]
-        dataset_path = "hub://aiswaryas/" + pathName + "1000"
+        dataset_path = "hub://aiswaryas/" + pathName + "3"
 
         db = DeepLake(dataset_path=dataset_path, read_only=True, embedding=embeddings)
 
@@ -427,7 +426,7 @@ def solveGithubIssue(ticket, history) -> Response:
             callback_manager=CallbackManager(
                 [StreamingGradioCallbackHandler(q)]
             ),
-            openai_api_key="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",
+            openai_api_key="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",
         )
         qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever,max_tokens_limit=8000)
 
@@ -455,9 +454,9 @@ def bot(history, **kwargs):
     print("Repo name in the bot: " + str(repoName))
     github = repoName[:-4]
     try:
-        embeddings = OpenAIEmbeddings(openai_api_key="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")
+        embeddings = OpenAIEmbeddings(openai_api_key="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")
         pathName = github.split('/')[-1]
-        dataset_path = "hub://aiswaryas/" + pathName + "1000"
+        dataset_path = "hub://aiswaryas/" + pathName + "3"
 
         db = DeepLake(dataset_path=dataset_path, read_only=True, embedding_function=embeddings)
 
@@ -477,7 +476,7 @@ def bot(history, **kwargs):
             callback_manager=CallbackManager(
                 [StreamingGradioCallbackHandler(q)]
             ),
-            openai_api_key="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",
+            openai_api_key="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",
         )
         qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)
         chat_history = []