Update url

app.py

@@ -81,7 +81,7 @@ global tickets
 global ticket_choices
 tickets = []
 
-repoName = "https://github.com/jerryjliu/llama_index.git"
+repoName = "https://github.com/gradio-app/gradio.git"
 
 embeddings = OpenAIEmbeddings(disallowed_special=())
 
@@ -109,6 +109,7 @@ def index_repo(textbox: str, dropdown: str) -> Response:
     else:
         repo = mapping[dropdown[0]]
 
+    print("REPO NAME: " + str(repo))
     pathName = git_clone(repo)
     root_dir = './' + pathName
 
@@ -185,7 +186,7 @@ def index_repo(textbox: str, dropdown: str) -> Response:
         )
 
     global ticket_choices, ticket_titles, tickets
-    repo = "/".join(repoName[:-4].split("/")[-2:])
+    repo = "/".join(repo[:-4].split("/")[-2:])
     tickets = fetchGithubIssues(repo, 10)
 
     # # Create the dropdown
@@ -326,34 +327,34 @@ def generateDocumentationPerFolder(dir, github):
             an overview of that function.
         """.format(dir, github)
 
-    return prompt
-    # try:
-    #     embeddings = OpenAIEmbeddings()
-    #     pathName = github.split('/')[-1]
-    #     dataset_path = "hub://aiswaryas/" + pathName
-
-    #     db = DeepLake(dataset_path=dataset_path, read_only=True, embedding_function=embeddings)
-
-    #     retriever = db.as_retriever()
-    #     retriever.search_kwargs['distance_metric'] = 'cos'
-    #     retriever.search_kwargs['fetch_k'] = 100
-    #     retriever.search_kwargs['maximal_marginal_relevance'] = True
-    #     retriever.search_kwargs['k'] = 20
-
-    #     # streaming_handler = kwargs.get('streaming_handler')
-    #     model = ChatOpenAI(
-    #         model_name='gpt-3.5-turbo-16k',
-    #         temperature=0.0,
-    #         verbose=True,
-    #         streaming=True,  # Pass `streaming=True` to make sure the client receives the data.
-    #     )
-    #     qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)
-    #     chat_history = []
-    #     return qa({"question": prompt, "chat_history": chat_history})["answer"]
-
-    # except Exception as e:
-    #     print (str(e))
-    #     return "Failed to generate documentation"
+    # return prompt
+    try:
+        embeddings = OpenAIEmbeddings()
+        pathName = github.split('/')[-1]
+        dataset_path = "hub://aiswaryas/" + pathName
+
+        db = DeepLake(dataset_path=dataset_path, read_only=True, embedding_function=embeddings)
+
+        retriever = db.as_retriever()
+        retriever.search_kwargs['distance_metric'] = 'cos'
+        retriever.search_kwargs['fetch_k'] = 100
+        retriever.search_kwargs['maximal_marginal_relevance'] = True
+        retriever.search_kwargs['k'] = 20
+
+        # streaming_handler = kwargs.get('streaming_handler')
+        model = ChatOpenAI(
+            model_name='gpt-3.5-turbo-16k',
+            temperature=0.0,
+            verbose=True,
+            streaming=True,  # Pass `streaming=True` to make sure the client receives the data.
+        )
+        qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)
+        chat_history = []
+        return qa({"question": prompt, "chat_history": chat_history})["answer"]
+
+    except Exception as e:
+        print (str(e))
+        return "Failed to generate documentation"
 
 
 def solveGithubIssue(ticket, history) -> Response: