Create app.py

app.py

@@ -0,0 +1,135 @@
+#import atexit
+import gradio as gr
+#from langchain.document_loaders import UnstructuredPDFLoader
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+#from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Pinecone
+import pinecone
+import requests
+import sys
+#from langchain.prompts.chat import (ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate)
+from langchain.chains.question_answering import load_qa_chain
+#from langchain.chains import RetrievalQA
+from langchain import PromptTemplate
+from langchain import HuggingFaceHub
+from PyPDF2 import PdfReader
+#from langchain.document_loaders import TextLoader
+#from sentence_transformers.util import semantic_search
+from pathlib import Path
+from time import sleep
+#import pandas as pd
+#import torch
+import os
+import random
+import string
+
+from dotenv import load_dotenv
+load_dotenv()
+
+file_path = os.path.join(os.getcwd(), "valuation.pdf")
+#loader = PyPDFLoader("60LEADERSONAI.pdf")
+#loader = PyPDFLoader(file_path)
+#data = loader.load()
+#text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+#db_texts = text_splitter.split_documents(data)
+
+data = PdfReader(file_path)
+raw_text = ''
+db_texts=''
+for i, page in enumerate(data.pages):
+    text = page.extract_text()
+    if text:
+        raw_text += text
+        text_splitter = RecursiveCharacterTextSplitter(        
+#            separator = "\n",
+            chunk_size = 1000,
+            chunk_overlap  = 100, #striding over the text
+            length_function = len,
+        )
+        db_texts = text_splitter.split_text(raw_text)
+
+class HFEmbeddings:
+    def __init__(self, api_url, headers):
+        self.api_url = api_url
+        self.headers = headers
+
+    def get_embeddings(self, texts):
+        response = requests.post(self.api_url, headers=self.headers, json={"inputs": texts, "options": {"wait_for_model": True}})
+        embeddings = response.json()
+        return embeddings
+
+    def embed_documents(self, texts):
+        embeddings = self.get_embeddings(texts)
+        return embeddings
+
+    def __call__(self, texts):
+        return self.embed_documents(texts)
+
+HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
+model_id = os.getenv('model_id')
+hf_token = os.getenv('hf_token')
+repo_id = os.getenv('repo_id')
+
+api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{model_id}"
+headers = {"Authorization": f"Bearer {hf_token}"}
+
+hf_embeddings = HFEmbeddings(api_url, headers)
+
+#Pinecone账号：
+PINECONE_API_KEY = os.getenv('PINECONE_API_KEY')
+PINECONE_ENVIRONMENT = os.getenv('PINECONE_ENVIRONMENT')
+PINECONE_INDEX_NAME = os.getenv('PINECONE_INDEX_NAME')
+#def generate_random_string(length):
+#    letters = string.ascii_letters
+#    random_string = ''.join(random.choice(letters) for _ in range(length))
+#    return random_string
+#random_string = generate_random_string(8)
+
+#def generate_random_string(length):
+#    letters = string.ascii_lowercase
+#    return ''.join(random.choice(letters) for i in range(length))      
+#random_string = generate_random_string(8)
+
+index_name = PINECONE_INDEX_NAME
+#namespace = random_string
+namespace = "HF-GRADIO-0909"
+
+#def exit_handler():
+#    pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENVIRONMENT)
+#    index_namespace_to_delete = pinecone.Index(index_name=index_name)
+#    index_namespace_to_delete.delete(delete_all=True, namespace=namespace)
+
+#atexit.register(exit_handler)
+
+pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENVIRONMENT)
+vector_db = Pinecone.from_texts(db_texts, hf_embeddings, index_name=index_name, namespace=namespace)
+#vector_db = Pinecone.from_texts([t.page_content for t in db_texts], hf_embeddings, index_name=index_name, namespace=namespace)
+#docsearch = Pinecone.from_texts([t.page_content for t in texts], embeddings, index_name=index_name, namespace=namespace)
+print("***********************************")
+print("Pinecone Vector/Embedding DB Ready.")
+print()
+
+llm = HuggingFaceHub(repo_id=repo_id,
+                     model_kwargs={"min_length":100,
+                                   "max_new_tokens":1024, "do_sample":True,
+                                   "temperature":0.1,
+                                   "top_k":50,
+                                   "top_p":0.95, "eos_token_id":49155})
+
+chain = load_qa_chain(llm=llm, chain_type="stuff")
+
+def run_chain(user_query):
+    if user_query !="" and not user_query.strip().isspace() and not user_query.isspace():
+      print("Your query:\n"+user_query)
+      vector_db_from_index = Pinecone.from_existing_index(index_name, hf_embeddings, namespace=namespace)
+      ss_results = vector_db_from_index.similarity_search(query=user_query, namespace=namespace, k=5)
+      initial_ai_response = chain.run(input_documents=ss_results, question=user_query)
+      temp_ai_response = initial_ai_response.partition('<|end|>')[0]
+      final_ai_response = temp_ai_response.replace('\n', '')
+      return final_ai_response
+    else:
+      print("Invalid inputs.")  
+
+iface = gr.Interface(fn=run_chain, inputs="text", outputs="text", title="AI Response")
+iface.launch()