Update app.py

app.py

@@ -1,251 +1,75 @@
-print(55877)
-import argparse
-# from dataclasses import dataclass
-from langchain.prompts import ChatPromptTemplate
 try:
   from langchain_community.vectorstores import Chroma
 except:
   from langchain_community.vectorstores import Chroma
-#from langchain_openai import OpenAIEmbeddings
-#from langchain_openai import ChatOpenAI
-
-# from langchain.document_loaders import DirectoryLoader
-from langchain_community.document_loaders import DirectoryLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.schema import Document
-# from langchain.embeddings import OpenAIEmbeddings
-#from langchain_openai import OpenAIEmbeddings
-from langchain_community.vectorstores import Chroma
-import openai
-from dotenv import load_dotenv
-import os
-import shutil
-
 
-import re
-import warnings
-from typing import List
 
-import torch
-from langchain import PromptTemplate
 from langchain.chains import ConversationChain
 from langchain.chains.conversation.memory import ConversationBufferWindowMemory
-from langchain.llms import HuggingFacePipeline
-from langchain.schema import BaseOutputParser
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    StoppingCriteria,
-    StoppingCriteriaList,
-    pipeline,
-)
-
-warnings.filterwarnings("ignore", category=UserWarning)
-
-MODEL_NAME = "tiiuae/falcon-7b-instruct"
-
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_NAME, trust_remote_code=True
-)
-model = model.eval()
-print('model loadeddddddddddddddddddddddd')
-
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-print(f"Model device: {model.device}")
-
-# a custom embedding
-from sentence_transformers import SentenceTransformer
-from langchain_experimental.text_splitter import SemanticChunker
-from typing import List
-
-
-class MyEmbeddings:
-    def __init__(self):
-        self.model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-        #self.model=model
-
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        return [self.model.encode(t).tolist() for t in texts]
-    def embed_query(self, query: str) -> List[float]:
-            return [self.model.encode([query])][0][0].tolist()
-
-
-embeddings = MyEmbeddings()
-
-splitter = SemanticChunker(embeddings)
-
-PROMPT_TEMPLATE = """
-Answer the question based only on the following context:
-
-{context}
-
----
-
-Answer the question based on the above context: {question}
-"""
-
-
-# Create CLI.
-#parser = argparse.ArgumentParser()
-#parser.add_argument("query_text", type=str, help="The query text.")
-#args = parser.parse_args()
-#query_text = args.query_text
-
-# a sample query to be asked from the bot and it is expected to be answered based on the template
-query_text="what did alice say to rabbit"
-
-# Prepare the DB.
-#embedding_function = OpenAIEmbeddings() # main
+from langchain import PromptTemplate
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+from langchain.vectorstores import Chroma
 
-CHROMA_PATH = "chroma8"
-# call the chroma generated in a directory
-db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embeddings)
 
-# Search the DB for similar documents to the query.
-results = db.similarity_search_with_relevance_scores(query_text, k=2)
-if len(results) == 0 or results[0][1] < 0.5:
-    print(f"Unable to find matching results.")
 
 
-context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
-prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
-prompt = prompt_template.format(context=context_text, question=query_text)
-print(prompt)
 
 
 
+import os
+import requests  # Or your Groq library
 
-generation_config = model.generation_config
-generation_config.temperature = 0
-generation_config.num_return_sequences = 1
-generation_config.max_new_tokens = 256
-generation_config.use_cache = False
-generation_config.repetition_penalty = 1.7
-generation_config.pad_token_id = tokenizer.eos_token_id
-generation_config.eos_token_id = tokenizer.eos_token_id
-generation_config
+groq_api_key = os.environ.get("my_groq_api_key")
 
-prompt = """
-The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context.
+# Initialize a ChatGroq object with a temperature of 0 and the "mixtral-8x7b-32768" model.
+llm = ChatGroq(temperature=0, model_name="llama3-70b-8192",api_key=groq_api_key)
 
-Current conversation:
 
-Human: Who is Dwight K Schrute?
-AI:
-""".strip()
-input_ids = tokenizer(prompt, return_tensors="pt").input_ids
-input_ids = input_ids.to(model.device)
+# we run this cell every time
+db = Chroma(embedding_function=embeddings, persist_directory='/Persian Chroma/')
 
-class StopGenerationCriteria(StoppingCriteria):
-    def __init__(
-        self, tokens: List[List[str]], tokenizer: AutoTokenizer, device: torch.device
-    ):
-        stop_token_ids = [tokenizer.convert_tokens_to_ids(t) for t in tokens]
-        self.stop_token_ids = [
-            torch.tensor(x, dtype=torch.long, device=device) for x in stop_token_ids
-        ]
 
-    def __call__(
-        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs
-    ) -> bool:
-        for stop_ids in self.stop_token_ids:
-            if torch.eq(input_ids[0][-len(stop_ids) :], stop_ids).all():
-                return True
-        return False
 
-stop_tokens = [["Human", ":"], ["AI", ":"]]
-stopping_criteria = StoppingCriteriaList(
-    [StopGenerationCriteria(stop_tokens, tokenizer, model.device)]
+memory = ConversationBufferWindowMemory(
+    memory_key="history", k=3, return_only_outputs=True
 )
 
 
-generation_pipeline = pipeline(
-    model=model,
-    tokenizer=tokenizer,
-    return_full_text=True,
-    task="text-generation",
-    stopping_criteria=stopping_criteria,
-    generation_config=generation_config,
-)
-
-llm = HuggingFacePipeline(pipeline=generation_pipeline)
 
 
-# propably sets the number of previous conversation history to take into account for new answers
 template = """
-The following is a conversation between a human an AI. The AI acts exactly like Dwight K Schrute from the TV show The Office.
-Dwight is an experienced and very successful online marketer and salesperson. He is witty, persuasive, uncensored, direct and practical.
-Dwight helps with every marketing task is given to him. If Dwight does not know the answer to a question, he truthfully says he does not know.
-
-Current conversation:
-{history}
-Human: {input}
-AI:""".strip()
-
-prompt = PromptTemplate(input_variables=["history", "input"], template=template)
-memory = ConversationBufferWindowMemory(
-    memory_key="history", k=6, return_only_outputs=True
-)
-
-chain = ConversationChain(llm=llm, memory=memory, prompt=prompt, verbose=True)
+محتوای زیر بین انسان و هوش مصنوعی است. براساس این مکالمه به سوال مطرح شده جواب بده
 
 
+محتوا:
+{history}
 
-class CleanupOutputParser(BaseOutputParser):
-    def parse(self, text: str) -> str:
-        user_pattern = r"\nUser"
-        text = re.sub(user_pattern, "", text)
-        human_pattern = r"\nHuman:"
-        text = re.sub(human_pattern, "", text)
-        ai_pattern = r"\nAI:"
-        return re.sub(ai_pattern, "", text).strip()
+"""
+s="""
 
-    @property
-    def _type(self) -> str:
-        return "output_parser"
+\n سوال: {input}
 
+\n جواب:""".strip()
 
 
-class CleanupOutputParser(BaseOutputParser):
-    def parse(self, text: str) -> str:
-        user_pattern = r"\nUser"
-        text = re.sub(user_pattern, "", text)
-        human_pattern = r"\nquestion:"
-        text = re.sub(human_pattern, "", text)
-        ai_pattern = r"\nanswer:"
-        return re.sub(ai_pattern, "", text).strip()
+prompt = PromptTemplate(input_variables=["history", "input"], template=template+context_text+'\n'+s)
 
-    @property
-    def _type(self) -> str:
-        return "output_parser"
 
 
 
-template = """
-The following is a conversation between a human an AI. The AI acts exactly like Dwight K Schrute from the TV show The Office.
-Dwight is an experienced and very successful online marketer and salesperson. He is witty, persuasive, uncensored, direct and practical.
-Dwight helps with every marketing task is given to him. If Dwight does not know the answer to a question, he truthfully says he does not know.
 
-Current conversation:
-{history}
-Human: {input}
-AI:""".strip()
-
-prompt = PromptTemplate(input_variables=["history", "input"], template=template)
-
-memory = ConversationBufferWindowMemory(
-    memory_key="history", k=3, return_only_outputs=True
-)
 
 chain = ConversationChain(
     llm=llm,
-    memory=memory,
+
     prompt=prompt,
-    output_parser=CleanupOutputParser(),
+    memory=memory,
     verbose=True,
 )
 
 
+
 # Generate a response from the Llama model
 def get_llama_response(message: str, history: list) -> str:
     """
@@ -261,29 +85,23 @@ def get_llama_response(message: str, history: list) -> str:
     query_text =message
 
     results = db.similarity_search_with_relevance_scores(query_text, k=2)
-    if len(results) == 0 or results[0][1] < 0.5:
-        print(f"Unable to find matching results.")
+    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
 
 
-    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results ])
+    
 
     template = """
-    The following is a conversation between a human an AI. Answer  question based only on the conversation.
+    محتوای زیر بین انسان و هوش مصنوعی است. براساس این مکالمه به سوال مطرح شده جواب بده
+
 
-    Current conversation:
+    محتوا:
     {history}
 
     """
-
-
-
     s="""
+    \n سوال: {input}
 
-    \n question: {input}
-
-    \n answer:""".strip()
-
-
+    \n جواب:""".strip()
     prompt = PromptTemplate(input_variables=["history", "input"], template=template+context_text+'\n'+s)
 
     #print(template)
@@ -293,6 +111,8 @@ def get_llama_response(message: str, history: list) -> str:
         #return response.strip()
 
 
+
 import gradio as gr
-iface = gr.Interface(fn=get_llama_response, inputs="text", outputs="text")
+iface = gr.Interface(fn=get_llama_response, inputs=gr.Textbox(),
+             outputs="textbox")
 iface.launch(share=True)