Update app.py

app.py

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+from datasets import load_dataset
+from datasets import Dataset
+from sentence_transformers import SentenceTransformer
+import faiss
+import time
+import json
+#import torch
+import pandas as pd
+from llama_cpp import Llama
+#from langchain_community.llms import LlamaCpp
+from threading import Thread
+from huggingface_hub import Repository, upload_file
+import os
+
+
+HF_TOKEN = os.getenv('HF_Token')
+#Log_Path="./Logfolder"
+logfile = 'DiabetesChatLog.txt'
+historylog = [{
+        "Prompt": '',
+        "Output": ''
+}]
+
+data = load_dataset("Namitg02/Test", split='train', streaming=False)
+#Returns a list of dictionaries, each representing a row in the dataset.
+length = len(data)
+
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+embedding_dim = embedding_model.get_sentence_embedding_dimension()
+# Returns dimensions of embedidng
+
+
+index =  faiss.IndexFlatL2(embedding_dim)
+data.add_faiss_index("embeddings", custom_index=index) 
+# adds an index column for the embeddings
+
+#question = "How can I reverse Diabetes?"
+
+SYS_PROMPT = """You are an assistant for answering questions.
+You are given the extracted parts of documents and a question. Provide a conversational answer.
+If you don't know the answer, just say "I do not know." Don't make up an answer. Don't repeat the SYS_PROMPT."""
+# Provides context of how to answer the question
+
+#llm_model = "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF", tinyllama-1.1b-chat-v1.0.Q5_K_M.gguf
+# TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF , TinyLlama/TinyLlama-1.1B-Chat-v0.6, andrijdavid/TinyLlama-1.1B-Chat-v1.0-GGUF"
+
+model = Llama(
+    model_path="./llama-2-7b-chat.Q4_K_M.gguf",
+ #   chat_format="llama-2",
+    n_gpu_layers = 0,
+    temperature=0.75,
+    n_ctx = 4096,
+    max_tokens=500,
+    top_p=0.95 #,
+ #   eos_tokens=terminators
+    # callback_manager=callback_manager,
+    # verbose=True,  # Verbose is required to pass to the callback manager
+    )
+#initiate model and tokenizer
+                        
+def search(query: str, k: int = 2 ):
+    """a function that embeds a new query and returns the most probable results"""
+    embedded_query = embedding_model.encode(query) # create embedding of a new query
+    scores, retrieved_examples = data.get_nearest_examples( # retrieve results
+        "embeddings", embedded_query, # compare our new embedded query with the dataset embeddings
+        k=k # get only top k results
+    )
+    return scores, retrieved_examples
+# returns scores (List[float]): the retrieval scores from either FAISS (IndexFlatL2 by default) and examples (dict) format
+# called by talk function that passes prompt
+
+def format_prompt(prompt,retrieved_documents,k,history,memory_limit=3):
+    """using the retrieved documents we will prompt the model to generate our responses"""   
+    PROMPT = f"Question:{prompt}\nContext:"
+    for idx in range(k) :
+        PROMPT+= f"{retrieved_documents['0'][idx]}\n"
+    print("historyinfo")
+    print(history)
+    if len(history) == 0:
+        return PROMPT
+    
+    if len(history) > memory_limit:
+        history = history[-memory_limit:]
+    
+    print("checkwohist")
+#    PROMPT = PROMPT + f"{history[0][0]} [/INST] {history[0][1]} </s>"
+#    print("checkwthhist")
+#    print(PROMPT)
+    # Handle conversation history
+    for user_message, bot_message in history[0:]:
+        PROMPT += f"<s>[INST] {user_message} [/INST] {bot_message} </s>"
+    print("checkwthhist2")
+    print(PROMPT)
+    return PROMPT
+
+
+# Called by talk function to add retrieved documents to the prompt. Keeps adding text of retrieved documents to string that are retreived
+
+def talk(prompt, history):
+    k = 2 # number of retrieved documents
+    scores , retrieved_documents = search(prompt, k) # get retrival scores and examples in dictionary format based on the prompt passed
+    print(retrieved_documents.keys())
+#    print("check4")
+    formatted_prompt = format_prompt(prompt,retrieved_documents,k,history,memory_limit=3) # create a new prompt using the retrieved documents
+    print("check5")
+    pd.options.display.max_colwidth = 4000
+#    print(retrieved_documents['0'])
+#    print(formatted_prompt)
+  #  formatted_prompt_with_history = add_history(formatted_prompt, history)
+
+   # formatted_prompt_with_history = formatted_prompt_with_history[:600] # to avoid memory issue
+#    print(formatted_prompt_with_history)
+    messages = [{"role":"system","content":SYS_PROMPT},{"role":"user","content":formatted_prompt}]  
+    print(messages)
+    # binding the system context and new prompt for LLM
+    # the chat template structure should be based on text generation model format
+
+# indicates the end of a sequence
+    stream = model.create_chat_completion(messages = messages, max_tokens=1000, stop=["</s>"], stream=False)
+#    print(f"{stream}")
+    print("check 7")
+    print(stream['choices'][0]['message']['content'])
+    return(stream['choices'][0]['message']['content'])
+#    text = ""
+#    for output in stream:
+#       text += output['choices'][0]['message']['content']  
+#       print(f"{output}")
+#       print("check3H")
+#       print(text)
+#       yield text 
+ 
+    
+    
+    # calling the model to generate response based on message/ input
+    # do_sample if set to True uses strategies to select the next token from the probability distribution over the entire vocabulary
+    # temperature controls randomness. more renadomness with higher temperature
+    # only the tokens comprising the top_p probability mass are considered for responses
+    # This output is a data structure containing all the information returned by generate(), but that can also be used as tuple or dictionary.
+ 
+
+    
+TITLE = "AI Copilot for Diabetes Patients"
+
+DESCRIPTION = "I provide answers to concerns related to Diabetes"
+
+import gradio as gr
+# Design chatbot
+demo = gr.ChatInterface(
+    fn=talk,
+    chatbot=gr.Chatbot(
+        show_label=True,
+        show_share_button=True,
+        show_copy_button=True,
+        likeable=True,
+        layout="bubble",
+        bubble_full_width=False,
+    ),
+    theme="Soft",
+    examples=[["what is Diabetes?"]],
+    title=TITLE,
+    description=DESCRIPTION,
+)
+# launch chatbot and calls the talk function which in turn calls other functions
+print("check14")
+#print(historylog)
+#memory_panda = pd.DataFrame(historylog)
+#Logfile = Dataset.from_pandas(memory_panda)
+#Logfile.push_to_hub("Namitg02/Logfile",token = HF_TOKEN)
+demo.launch()