Create app.py

app.py

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+
+from llama_index import VectorStoreIndex,download_loader, VectorStoreIndex, ServiceContext, StorageContext, load_index_from_storage
+from pathlib import Path
+from github import Github
+import os
+import shutil
+import openai
+import gradio as gr
+
+from pathlib import Path
+from llama_index import download_loader
+
+"""# Github Configeration"""
+
+openai.api_key = os.environ.get("OPENAPI_API_KEY")
+
+# username = 'Akhil-Sharma30'
+
+
+"""# Reading the Files for LLM Model"""
+
+
+# Specify the path to the repository
+repo_dir = "/content/Akhil-Sharma30.github.io"
+
+# Check if the repository exists and delete it if it does
+if os.path.exists(repo_dir):
+    shutil.rmtree(repo_dir)
+
+
+# def combine_md_files(folder_path):
+#     MarkdownReader = download_loader("MarkdownReader")
+#     loader = MarkdownReader()
+
+#     md_files = [file for file in folder_path.glob('*.md')]
+#     documents = None
+
+#     for file_path in md_files:
+#         document = loader.load_data(file=file_path)
+#         documents += document
+
+#     return documents
+
+# folder_path = Path('/content/Akhil-Sharma30.github.io/content')
+#combined_documents = combine_md_files(folder_path)
+
+# combined_documents will be a list containing the contents of all .md files in the folder
+
+MarkdownReader = download_loader("MarkdownReader")
+
+loader = MarkdownReader()
+document1 = loader.load_data(file=Path('Akhil-Sharma30.github.io/assets/README.md'))
+document2 = loader.load_data(file=Path('Akhil-Sharma30.github.io/content/about.md'))
+document3 = loader.load_data(file=Path('Akhil-Sharma30.github.io/content/cv.md'))
+document4 = loader.load_data(file=Path('Akhil-Sharma30.github.io/content/post.md'))
+document5 = loader.load_data(file=Path('Akhil-Sharma30.github.io/content/opensource.md'))
+document6 = loader.load_data(file=Path('Akhil-Sharma30.github.io/content/supervised.md'))
+
+data = document1+ document2 + document3+ document4 + document5+document6
+
+
+"""# Vector Embedding"""
+
+index = VectorStoreIndex.from_documents(data)
+
+query_engine = index.as_query_engine()
+response = query_engine.query("know akhil?")
+print(response)
+
+response = query_engine.query("what is name of the person?")
+print(response)
+
+"""# ChatBot Interface"""
+
+def chat(chat_history, user_input):
+
+  bot_response = query_engine.query(user_input)
+  #print(bot_response)
+  response = ""
+  for letter in ''.join(bot_response.response): #[bot_response[i:i+1] for i in range(0, len(bot_response), 1)]:
+      response += letter + ""
+      yield chat_history + [(user_input, response)]
+
+with gr.Blocks() as demo:
+    gr.Markdown('# Robotic Akhil')
+    gr.Markdown('## "Innovating Intelligence - Unveil the secrets of a cutting-edge ChatBot project that introduces you to the genius behind the machine. 👨🏻‍💻😎')
+    gr.Markdown('> Hint: Akhil 2.0')
+    gr.Markdown('## Some question you can ask to test Bot:')
+    gr.Markdown('#### :) know akhil?')
+    gr.Markdown('#### :) write about my work at Agnisys?')
+    gr.Markdown('#### :) write about my work at IIT Delhi?')
+    gr.Markdown('#### :) was work in P1 Virtual Civilization Initiative opensource?')
+    gr.Markdown('#### many more......')
+    with gr.Tab("Knowledge Bot"):
+#inputbox = gr.Textbox("Input your text to build a Q&A Bot here.....")
+          chatbot = gr.Chatbot()
+          message = gr.Textbox ("know akhil?")
+          message.submit(chat, [chatbot, message], chatbot)
+
+demo.queue().launch(share=True)
+
+
+"""# **Github Setup**"""
+
+
+
+"""## Launch Phoenix
+
+Define your knowledge base dataset with a schema that specifies the meaning of each column (features, predictions, actuals, tags, embeddings, etc.). See the [docs](https://docs.arize.com/phoenix/) for guides on how to define your own schema and API reference on `phoenix.Schema` and `phoenix.EmbeddingColumnNames`.
+"""
+
+# # get a random sample of 500 documents (including retrieved documents)
+# # this will be handled by by the application in a coming release
+# num_sampled_point = 500
+# retrieved_document_ids = set(
+#     [
+#         doc_id
+#         for doc_ids in query_df[":feature.[str].retrieved_document_ids:prompt"].to_list()
+#         for doc_id in doc_ids
+#     ]
+# )
+# retrieved_document_mask = database_df["document_id"].isin(retrieved_document_ids)
+# num_retrieved_documents = len(retrieved_document_ids)
+# num_additional_samples = num_sampled_point - num_retrieved_documents
+# unretrieved_document_mask = ~retrieved_document_mask
+# sampled_unretrieved_document_ids = set(
+#     database_df[unretrieved_document_mask]["document_id"]
+#     .sample(n=num_additional_samples, random_state=0)
+#     .to_list()
+# )
+# sampled_unretrieved_document_mask = database_df["document_id"].isin(
+#     sampled_unretrieved_document_ids
+# )
+# sampled_document_mask = retrieved_document_mask | sampled_unretrieved_document_mask
+# sampled_database_df = database_df[sampled_document_mask]
+
+# database_schema = px.Schema(
+#     prediction_id_column_name="document_id",
+#     prompt_column_names=px.EmbeddingColumnNames(
+#         vector_column_name="text_vector",
+#         raw_data_column_name="text",
+#     ),
+# )
+# database_ds = px.Dataset(
+#     dataframe=sampled_database_df,
+#     schema=database_schema,
+#     name="database",
+# )
+
+"""Define your query dataset. Because the query dataframe is in OpenInference format, Phoenix is able to infer the meaning of each column without a user-defined schema by using the `phoenix.Dataset.from_open_inference` class method."""
+
+# query_ds = px.Dataset.from_open_inference(query_df)
+
+"""Launch Phoenix. Follow the instructions in the cell output to open the Phoenix UI."""
+
+# session = px.launch_app(primary=query_ds, corpus=database_ds)
+