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README.md

@@ -1,12 +1,13 @@
----
-title: Testcode1
-emoji: 📈
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 5.6.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: test code
+emoji: 🙄
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+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")
+"""
+llm = None
+model = None
+embedd_bk = None
+df_bk = None
+
+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)  
+
+    df_user=pd.DataFrame(columns=["輸入內��","形容詞1", "形容詞2", "形容詞3", "角色1", "角色2", "角色3"]) #
+
+    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()

pre-requirements.txt

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+pip>=24.1

requirements.txt

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+huggingface_hub
+scikit-build-core
+llama-cpp-python
+scikit-learn
+numpy<2
+langchain