Update app.py

app.py

@@ -1,98 +1,24 @@
-# import os
-# import sys
-# import openai
-# from langchain.chains import ConversationalRetrievalChain, RetrievalQA
-# from langchain.chat_models import ChatOpenAI
-# from langchain.document_loaders import DirectoryLoader, TextLoader
-# from langchain.embeddings import OpenAIEmbeddings
-# from langchain.indexes import VectorstoreIndexCreator
-# from langchain.indexes.vectorstore import VectorStoreIndexWrapper
-# from langchain.llms import OpenAI
-# from langchain.text_splitter import CharacterTextSplitter
-
-# __import__('pysqlite3')
-# import sys
-# sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
-
-# from langchain.vectorstores import Chroma
-# import gradio as gr
-
-# os.environ["OPENAI_API_KEY"] = os.getenv("OPENAPIKEY")
-
-# docs = []
-
-# for f in os.listdir("multiple_docs"):
-#     if f.endswith(".pdf"):
-#         pdf_path = "./multiple_docs/" + f
-#         loader = PyPDFLoader(pdf_path)
-#         docs.extend(loader.load())
-#     elif f.endswith('.docx') or f.endswith('.doc'):
-#         doc_path = "./multiple_docs/" + f
-#         loader = Docx2txtLoader(doc_path)
-#         docs.extend(loader.load())
-#     elif f.endswith('.txt'):
-#         text_path = "./multiple_docs/" + f
-#         loader = TextLoader(text_path)
-#         docs.extend(loader.load())
-
-# splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10)
-# docs = splitter.split_documents(docs)
-
-# # Convert the document chunks to embedding and save them to the vector store
-# vectorstore = Chroma.from_documents(docs, embedding=OpenAIEmbeddings(), persist_directory="./data")
-# vectorstore.persist()
-
-# chain = ConversationalRetrievalChain.from_llm(
-#     ChatOpenAI(temperature=0.7, model_name='gpt-3.5-turbo'),
-#     retriever=vectorstore.as_retriever(search_kwargs={'k': 6}),
-#     return_source_documents=True,
-#     verbose=False
-# )
-
-# chat_history = []
-
-# with gr.Blocks() as demo:
-#     chatbot = gr.Chatbot([("", "Hello, I'm Thierry Decae's chatbot, you can ask me any recruitment related questions such as my previous or most recent experience, where I'm eligible to work, when I can start work, what NLP skills I have, and much more! you can chat with me directly in multiple languages")],avatar_images=["./multiple_docs/Guest.jpg","./multiple_docs/Thierry Picture.jpg"])
-#     msg = gr.Textbox()
-#     clear = gr.Button("Clear")
-#     chat_history = []
-
-#     def user(query, chat_history):
-#         # print("User query:", query)
-#         # print("Chat history:", chat_history)
-
-#         # Convert chat history to list of tuples
-#         chat_history_tuples = []
-#         for message in chat_history:
-#             chat_history_tuples.append((message[0], message[1]))
-
-#         # Get result from QA chain
-#         result = chain({"question": query, "chat_history": chat_history_tuples})
-
-#         # Append user message and response to chat history
-#         chat_history.append((query, result["answer"]))
-#         # print("Updated chat history:", chat_history)
-
-#         return gr.update(value=""), chat_history
-
-#     msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False)
-#     clear.click(lambda: None, None, chatbot, queue=False)
-
-# demo.launch(debug=True)
-
 import os
 import sys
-from langchain.chains import ConversationalRetrievalChain
-from langchain.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader
+import openai
+from langchain.chains import ConversationalRetrievalChain, RetrievalQA
+from langchain.chat_models import ChatOpenAI
+from langchain.document_loaders import DirectoryLoader, TextLoader
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.indexes.vectorstore import VectorStoreIndexWrapper
+from langchain.llms import OpenAI
 from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import Chroma
-import gradio as gr
-from transformers import pipeline
-from sentence_transformers import SentenceTransformer
 
 __import__('pysqlite3')
+import sys
 sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
 
+from langchain.vectorstores import Chroma
+import gradio as gr
+
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAPIKEY")
+
 docs = []
 
 for f in os.listdir("multiple_docs"):
@@ -112,31 +38,12 @@ for f in os.listdir("multiple_docs"):
 splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10)
 docs = splitter.split_documents(docs)
 
-# Extract the content from documents and create embeddings
-embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-texts = [doc.page_content for doc in docs]
-embeddings = embedding_model.encode(texts).tolist()  # Convert numpy arrays to lists
-
-# Create a Chroma vector store and add documents and their embeddings
-vectorstore = Chroma(persist_directory="./db", embedding_function=embedding_model.encode)
-vectorstore.add_texts(texts=texts, metadatas=[{"id": i} for i in range(len(texts))], embeddings=embeddings)
+# Convert the document chunks to embedding and save them to the vector store
+vectorstore = Chroma.from_documents(docs, embedding=OpenAIEmbeddings(), persist_directory="./data")
 vectorstore.persist()
 
-# Load the Hugging Face model for text generation
-generator = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
-
-class HuggingFaceLLMWrapper:
-    def __init__(self, generator):
-        self.generator = generator
-    
-    def __call__(self, prompt, max_length=512):
-        result = self.generator(prompt, max_length=max_length, num_return_sequences=1)
-        return result[0]['generated_text']
-
-llm = HuggingFaceLLMWrapper(generator)
-
 chain = ConversationalRetrievalChain.from_llm(
-    llm,
+    ChatOpenAI(temperature=0.7, model_name='gpt-3.5-turbo'),
     retriever=vectorstore.as_retriever(search_kwargs={'k': 6}),
     return_source_documents=True,
     verbose=False
@@ -145,12 +52,15 @@ chain = ConversationalRetrievalChain.from_llm(
 chat_history = []
 
 with gr.Blocks() as demo:
-    chatbot = gr.Chatbot([("", "Hello, I'm Thierry Decae's chatbot, you can ask me any recruitment related questions such as my previous or most recent experience, where I'm eligible to work, when I can start work, what NLP skills I have, and much more! you can chat with me directly in multiple languages")], avatar_images=["./multiple_docs/Guest.jpg","./multiple_docs/Thierry Picture.jpg"])
+    chatbot = gr.Chatbot([("", "Hello, I'm Thierry Decae's chatbot, you can ask me any recruitment related questions such as my previous or most recent experience, where I'm eligible to work, when I can start work, what NLP skills I have, and much more! you can chat with me directly in multiple languages")],avatar_images=["./multiple_docs/Guest.jpg","./multiple_docs/Thierry Picture.jpg"])
     msg = gr.Textbox()
     clear = gr.Button("Clear")
     chat_history = []
 
     def user(query, chat_history):
+        # print("User query:", query)
+        # print("Chat history:", chat_history)
+
         # Convert chat history to list of tuples
         chat_history_tuples = []
         for message in chat_history:
@@ -161,6 +71,7 @@ with gr.Blocks() as demo:
 
         # Append user message and response to chat history
         chat_history.append((query, result["answer"]))
+        # print("Updated chat history:", chat_history)
 
         return gr.update(value=""), chat_history
 
@@ -169,6 +80,95 @@ with gr.Blocks() as demo:
 
 demo.launch(debug=True)
 
+# import os
+# import sys
+# from langchain.chains import ConversationalRetrievalChain
+# from langchain.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader
+# from langchain.text_splitter import CharacterTextSplitter
+# from langchain.vectorstores import Chroma
+# import gradio as gr
+# from transformers import pipeline
+# from sentence_transformers import SentenceTransformer
+
+# __import__('pysqlite3')
+# sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
+
+# docs = []
+
+# for f in os.listdir("multiple_docs"):
+#     if f.endswith(".pdf"):
+#         pdf_path = "./multiple_docs/" + f
+#         loader = PyPDFLoader(pdf_path)
+#         docs.extend(loader.load())
+#     elif f.endswith('.docx') or f.endswith('.doc'):
+#         doc_path = "./multiple_docs/" + f
+#         loader = Docx2txtLoader(doc_path)
+#         docs.extend(loader.load())
+#     elif f.endswith('.txt'):
+#         text_path = "./multiple_docs/" + f
+#         loader = TextLoader(text_path)
+#         docs.extend(loader.load())
+
+# splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10)
+# docs = splitter.split_documents(docs)
+
+# # Extract the content from documents and create embeddings
+# embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+# texts = [doc.page_content for doc in docs]
+# embeddings = embedding_model.encode(texts).tolist()  # Convert numpy arrays to lists
+
+# # Create a Chroma vector store and add documents and their embeddings
+# vectorstore = Chroma(persist_directory="./db", embedding_function=embedding_model.encode)
+# vectorstore.add_texts(texts=texts, metadatas=[{"id": i} for i in range(len(texts))], embeddings=embeddings)
+# vectorstore.persist()
+
+# # Load the Hugging Face model for text generation
+# generator = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
+
+# class HuggingFaceLLMWrapper:
+#     def __init__(self, generator):
+#         self.generator = generator
+    
+#     def __call__(self, prompt, max_length=512):
+#         result = self.generator(prompt, max_length=max_length, num_return_sequences=1)
+#         return result[0]['generated_text']
+
+# llm = HuggingFaceLLMWrapper(generator)
+
+# chain = ConversationalRetrievalChain.from_llm(
+#     llm,
+#     retriever=vectorstore.as_retriever(search_kwargs={'k': 6}),
+#     return_source_documents=True,
+#     verbose=False
+# )
+
+# chat_history = []
+
+# with gr.Blocks() as demo:
+#     chatbot = gr.Chatbot([("", "Hello, I'm Thierry Decae's chatbot, you can ask me any recruitment related questions such as my previous or most recent experience, where I'm eligible to work, when I can start work, what NLP skills I have, and much more! you can chat with me directly in multiple languages")], avatar_images=["./multiple_docs/Guest.jpg","./multiple_docs/Thierry Picture.jpg"])
+#     msg = gr.Textbox()
+#     clear = gr.Button("Clear")
+#     chat_history = []
+
+#     def user(query, chat_history):
+#         # Convert chat history to list of tuples
+#         chat_history_tuples = []
+#         for message in chat_history:
+#             chat_history_tuples.append((message[0], message[1]))
+
+#         # Get result from QA chain
+#         result = chain({"question": query, "chat_history": chat_history_tuples})
+
+#         # Append user message and response to chat history
+#         chat_history.append((query, result["answer"]))
+
+#         return gr.update(value=""), chat_history
+
+#     msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False)
+#     clear.click(lambda: None, None, chatbot, queue=False)
+
+# demo.launch(debug=True)
+