Update openAI key and documentation

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'
 
 
@@ -218,7 +218,7 @@ 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
 
@@ -234,14 +234,14 @@ def answer_questions(question: str, github: str, **kwargs) -> Response:
         q = SimpleQueue()
 
         model = ChatOpenAI(
-            model_name='gpt-4',
+            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.
             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 = []
@@ -342,7 +342,7 @@ 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
@@ -358,11 +358,11 @@ def generateDocumentationPerFolder(dir, github):
 
         # streaming_handler = kwargs.get('streaming_handler')
         model = ChatOpenAI(
-            model_name='gpt-4',
+            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.
-            openai_api_key="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",
+            openai_api_key="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",
         )
         qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)
         chat_history = []
@@ -405,7 +405,7 @@ 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
 
@@ -420,14 +420,14 @@ def solveGithubIssue(ticket, history) -> Response:
 
         q = SimpleQueue()
         model = ChatOpenAI(
-            model_name='gpt-4',
+            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.
             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)
 
@@ -455,7 +455,7 @@ 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
 
@@ -470,14 +470,14 @@ def bot(history, **kwargs):
 
         q = SimpleQueue()
         model = ChatOpenAI(
-            model_name='gpt-4',
+            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.
             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 = []
@@ -585,28 +585,39 @@ with gr.Blocks() as demo:
             """)
 
             #   docs = generateDocumentationPerFolder("overview", repo_name)
-            markdown = gr.Markdown()
 
-            def button_click_callback(markdown):
-                print("IN BUTTON CLICK CALLBACK")
-                docs = generateDocumentationPerFolder("overview", repoName[:-4])
-                markdown.update(docs)
-
-            # Generate the left column buttons and their names and wrap each one in a function
-            with gr.Row():
-                with gr.Column(scale=.5, min_width=300):
-                    dirNames = generateFolderNamesForRepo(repoName[:-4])
-                    print(dirNames)
-                    buttons = [gr.Button(folder_name, onclick=button_click_callback) for folder_name in dirNames]
-                    for btn, folder_name in zip(buttons, dirNames):
-                        btn.click(button_click_callback, [markdown], [markdown] )
-
-
-                # Generate the overall documentation for the main bubble at the same time
-                print("REPO NAME IN DOCS: " + str(repoName[:-4]))
-                with gr.Column(scale=2, min_width=300):
-                    docs = generateDocumentationPerFolder("overview", repoName[:-4])
-                    markdown.update(docs)
+            # For now let's just display all of the docs in one big file
+            allDocs = ""
+            dirNames = generateFolderNamesForRepo(repoName[:-4])
+            for dir in dirNames:
+                if dir[0] != ".":
+                    allDocs += generateDocumentationPerFolder(dir, repoName[:-4]) + '\n\n'
+
+            gr.Markdown(allDocs)
+
+
+            # def button_click_callback(markdown):
+            #     print("IN BUTTON CLICK CALLBACK")
+            #     docs = generateDocumentationPerFolder("overview", repoName[:-4])
+            #     markdown.update(docs)
+
+            # markdown = gr.Markdown()
+            # # Generate the left column buttons and their names and wrap each one in a function
+            # with gr.Row():
+            #     with gr.Column(scale=.5, min_width=300):
+            #         dirNames = generateFolderNamesForRepo(repoName[:-4])
+            #         print(dirNames)
+            #         buttons = [gr.Button(folder_name) for folder_name in dirNames]
+            #         for btn, folder_name in zip(buttons, dirNames):
+            #             btn.click(button_click_callback, [markdown], [markdown] )
+
+
+            #     # Generate the overall documentation for the main bubble at the same time
+            #     print("REPO NAME IN DOCS: " + str(repoName[:-4]))
+            #     with gr.Column(scale=2, min_width=300):
+            #         docs = generateDocumentationPerFolder("overview", repoName[:-4])
+            #         markdown.update(docs)
+            #         markdown.render()
 
             # For each folder, generate a diagram and 2-3 prompts that dive deeper into explaining content