%%blocks
import gradio as gr
from llama_cpp import Llama
from langchain_community.llms import LlamaCpp
from langchain.prompts import PromptTemplate
import llama_cpp
from langchain.callbacks.manager import CallbackManager
from sentence_transformers import SentenceTransformer
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import numpy as np
import pandas as pd
import re
import os
from sklearn.metrics.pairwise import cosine_similarity
model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2',device='cpu')
llm = LlamaCpp(
model_path=r"C:\Users\Cora\.cache\lm-studio\models\YC-Chen\Breeze-7B-Instruct-v1_0-GGUF\breeze-7b-instruct-v1_0-q4_k_m.gguf",
n_gpu_layers=100,
n_batch=512,
n_ctx=3000,
f16_kv=True,
callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
verbose=False,
)
embedd_bk=pd.read_pickle(r"C:\Users\Cora\推薦系統實作\bk_description1_角色形容詞_677.pkl")
df_bk=pd.read_excel(r"C:\Users\Cora\推薦系統實作\bk_description1_角色形容詞.xlsx")
def invoke_with_temperature(prompt, temperature=0.4):
return llm.invoke(prompt, temperature=temperature)
def process_user_input(message):
user_mental_state4= PromptTemplate(
input_variables=["input"],
template="""[INST][/INST]"""
)
user_character= PromptTemplate(
input_variables=["input"],
template="""[INST][/INST]"""
)
df_user=pd.DataFrame(columns=["輸入內容","形容詞1", "形容詞2", "形容詞3", "角色1", "角色2", "角色3"])
prompt_value1=user_mental_state4.invoke({"input":message})
string=invoke_with_temperature(prompt_value1)
#print("\n")
# 將字符串分割為名詞
adjectives = [adj.strip() for adj in re.split('[,、，]', string)]
index=len(df_user)
df_user.loc[index, '輸入內容'] = message
# 確保形容詞數量符合欄位數量
if len(adjectives) == 3:
df_user.loc[index, '形容詞1'] = adjectives[0]
df_user.loc[index, '形容詞2'] = adjectives[1]
df_user.loc[index, '形容詞3'] = adjectives[2]
df_user.to_excel("user_gradio系統.xlsx")
return df_user
def embedd_df_user(df_user):
columns_to_encode=df_user.loc[:,["形容詞1", "形容詞2", "形容詞3"]]
# 初始化一個空的 DataFrame，用來存儲向量化結果
embedd_user=df_user[["輸入內容"]]
#user_em= user_em.assign(形容詞1=None, 形容詞2=None, 形容詞3=None,角色1=None,角色2=None,角色3=None)
embedd_user= embedd_user.assign(形容詞1=None, 形容詞2=None, 形容詞3=None)
# 遍歷每一個單元格，將結果存入新的 DataFrame 中
i=len(df_user)-1
for col in columns_to_encode:
#print(i,col)
# 將每個單元格的內容進行向量化
embedd_user.at[i, col] = model.encode(df_user.at[i, col])
embedd_user.to_pickle(r"C:\Users\Cora\推薦系統實作\user_gradio系統.pkl")
return embedd_user
def top_n_books_by_average(df, n=3):
# 根据 `average` 列降序排序
sorted_df = df.sort_values(by='average', ascending=False)
# 选择前 N 行
top_n_df = sorted_df.head(n)
# 提取书名列
top_books = top_n_df['書名'].tolist()
return top_books,sorted_df
def similarity(embedd_user,embedd_bk,df_bk):
df_similarity= pd.DataFrame(df_bk[['書名',"內容簡介","URL","形容詞1", "形容詞2", "形容詞3", '角色1', '角色2', '角色3']])
df_similarity['average'] = np.nan
#for p in range(len(embedd_user)):
index=len(embedd_user)-1
for k in range(len(embedd_bk)):
list=[]
for i in range(1,4):
for j in range(3,6):
vec1=embedd_user.iloc[index,i]#i是第i個形容詞，數字是第幾個是使用者輸入
vec2=embedd_bk.iloc[k,j]
similarity = cosine_similarity([vec1], [vec2])
list.append(similarity[0][0])
# 计算总和
total_sum = sum(list)
# 计算数量
count = len(list)
# 计算平均值
average = total_sum / count
df_similarity.loc[k,'average']=average
top_books,sorted_df = top_n_books_by_average(df_similarity)
return sorted_df
def filter(sorted_df):
filter_prompt4 = PromptTemplate(
input_variables=["mental_issue", "user_identity"," book","book_reader", "book_description"],
template="""[INST][/INST]"""
)
df_filter=sorted_df.iloc[:20,:]
df_filter = df_filter.reset_index(drop=True)
df_filter=df_filter.assign(推薦=None)
p=len(df_user)-1
for k in range(len(df_filter)):
word=df_user["輸入內容"].iloc[p]
#book_reader = df_filter["角色1"].iloc[p] + "or" + df_filter["角色2"].iloc[p] + "or" + df_filter["角色3"].iloc[p]
book=df_filter["書名"].iloc[k]
book_reader = df_filter["角色1"].iloc[k]
user_identity = df_user["角色1"].iloc[p]
mental_issue=df_user["形容詞1"].iloc[p]+"、"+df_user["形容詞2"].iloc[p]+"、"+df_user["形容詞3"].iloc[p]
book_description=df_filter["形容詞1"].iloc[k]+"、"+df_filter["形容詞2"].iloc[k]+"、"+df_filter["形容詞3"].iloc[k]
print(book_reader)
print(user_identity)
#output = filter_prompt1.invoke({"user_identity": user_identity, "book_reader": book_reader})
output = filter_prompt4.invoke({"mental_issue":mental_issue,"user_identity": user_identity, "book":book,"book_description":book_description,"book_reader": book_reader})
string2=invoke_with_temperature(output)
df_filter.loc[k, '推薦'] =string2
df_recommend=df_filter[df_filter["推薦"].str.strip() == "是"]
return df_recommend
def output_content(df_recommend):
content_prompt = PromptTemplate(
input_variables=["content"],
template="""[INST][/INST]"""
)
a=0
title=df_recommend.loc[a,"書名"]
prompt_value1=recommend_prompt.invoke({"title":title,"URL":URL,"summary":summary})
recommend_prompt = PromptTemplate(
input_variables=["title"],
template=
)
prompt_value1=recommend_prompt.invoke({"title":title})
output=invoke_with_temperature(prompt_value1,temperature=0.4)
return output
def main_pipeline(message,history):
df_user=process_user_input(message)
embedd_user=embedd_df_user(df_user)
sorted_df=similarity(embedd_user,embedd_bk,df_bk)
df_filter=filter(sorted_df)
final=output_content(df_filter)
return final
demo=gr.ChatInterface(main_pipeline)
if __name__ == "__main__":
demo.launch()
This is my whole code. I’m trying to build a book recommendation app on Gradio.
However, I keep meeting the problem of NameError: name ‘process_user_input’ is not defined.
But I have already define it before main_pipeline. I would be grateful if someone could help me figure it out. Thank you so much!