Delete app_old.py

app_old.py

@@ -1,190 +0,0 @@
-# import gradio as gr
-# from huggingface_hub import InferenceClient
-
-# """
-# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-# """
-# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-# def respond(
-#     message,
-#     history: list[tuple[str, str]],
-#     system_message,
-#     max_tokens,
-#     temperature,
-#     top_p,
-# ):
-#     messages = [{"role": "system", "content": system_message}]
-
-#     for val in history:
-#         if val[0]:
-#             messages.append({"role": "user", "content": val[0]})
-#         if val[1]:
-#             messages.append({"role": "assistant", "content": val[1]})
-
-#     messages.append({"role": "user", "content": message})
-
-#     response = ""
-
-#     for message in client.chat_completion(
-#         messages,
-#         max_tokens=max_tokens,
-#         stream=True,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-#         token = message.choices[0].delta.content
-
-#         response += token
-#         yield response
-
-# """
-# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-# """
-# demo = gr.ChatInterface(
-#     respond,
-#     additional_inputs=[
-#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-#         gr.Slider(
-#             minimum=0.1,
-#             maximum=1.0,
-#             value=0.95,
-#             step=0.05,
-#             label="Top-p (nucleus sampling)",
-#         ),
-#     ],
-# )
-
-
-# if __name__ == "__main__":
-#     demo.launch()
-
-
-
-
-import gradio as gr
-import torch
-from transformers import RagRetriever, RagSequenceForGeneration, AutoTokenizer
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-def strip_title(title):
-    if title.startswith('"'):
-        title = title[1:]
-    if title.endswith('"'):
-        title = title[:-1]
-    return title
-
-def retrieved_info(rag_model, query):
-    # Tokenize query
-    retriever_input_ids = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
-        [query],
-        return_tensors="pt",
-        padding=True,
-        truncation=True,
-    )["input_ids"].to(device)
-
-    # Retrieve documents
-    question_enc_outputs = rag_model.rag.question_encoder(retriever_input_ids)
-    question_enc_pool_output = question_enc_outputs[0]
-
-    result = rag_model.retriever(
-        retriever_input_ids,
-        question_enc_pool_output.cpu().detach().to(torch.float32).numpy(),
-        prefix=rag_model.rag.generator.config.prefix,
-        n_docs=rag_model.config.n_docs,
-        return_tensors="pt",
-    )
-
-    # Display retrieved documents including URLs
-    all_docs = rag_model.retriever.index.get_doc_dicts(result.doc_ids)
-    retrieved_context = []
-    for docs in all_docs:
-        titles = [strip_title(title) for title in docs["title"]]
-        texts = docs["text"]
-        for title, text in zip(titles, texts):
-            #print(f"Title: {title}")
-            #print(f"Context: {text}")
-            retrieved_context.append(f"{title}: {text}")
-
-    answer = retrieved_context
-    return answer
-
-
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens ,
-    temperature,
-    top_p,
-):
-    # Load model
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-    dataset_path = "./sample/my_knowledge_dataset"
-    index_path = "./sample/my_knowledge_dataset_hnsw_index.faiss"
-
-    tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq")
-    retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", index_name="custom",
-                                             passages_path = dataset_path,
-                                             index_path = index_path,
-                                             n_docs = 5)
-    rag_model = RagSequenceForGeneration.from_pretrained('facebook/rag-sequence-nq', retriever=retriever)
-    rag_model.retriever.init_retrieval()
-    rag_model.to(device)
-
-    if message:  # If there's a user query
-        response = retrieved_info(rag_model, message)  # Get the answer from your local FAISS and Q&A model
-        return response[0]
-
-    # In case no message, return an empty string
-    return ""
-
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-# Custom title and description
-title = "🧠 Welcome to Your AI Knowledge Assistant"
-description = """
-HI!!, I am a chatbot, I retrieves relevant information from a custom dataset using RAG. Ask any question, and let me assist you.
-My capabilities and knowledge is limited right now because of computational resources. Originally I can acess more than a million files 
-from my knowledge-base but, right now, I am limited to less than 1000 files. LET'S BEGGINNNN......
-"""
-
-demo = gr.ChatInterface(
-    respond,
-    type = 'messages',
-    additional_inputs=[
-        gr.Textbox(value="You are a helpful and friendly assistant.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-    title=title,
-    description=description,
-    textbox=gr.Textbox(placeholder=["'What is the future of AI?' or 'App Development'"]),
-    examples=[["✨Future of AI"], ["📱App Development"]],
-    example_icons=["🤖", "📱"],
-    theme="compact",
-)
-
-
-if __name__ == "__main__":
-    demo.launch(share = True )