pls chat

app.py

@@ -1,30 +1,101 @@
+# import gradio as gr
+# import pandas as pd
+# from langchain.embeddings import HuggingFaceEmbeddings
+# from langchain.vectorstores import Chroma, faiss
+# from langchain_community.llms import HuggingFaceEndpoint, HuggingFaceHub
+# from langchain.chains import LLMChain
+# from langchain_community.document_loaders.csv_loader import CSVLoader
+# from langchain_community.document_loaders import PyPDFLoader
+# from langchain.text_splitter import CharacterTextSplitter
+# from langchain_community.document_loaders import TextLoader
+# from langchain_community import vectorstores
+# from langchain.prompts import PromptTemplate
+# from langchain.chains import RetrievalQA
+# from langchain.memory import ConversationBufferMemory
+# from langchain.chains import ConversationalRetrievalChain
+# from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
+# from langchain.vectorstores import DocArrayInMemorySearch
+# from langchain.document_loaders import TextLoader
+# from langchain.chains import RetrievalQA, ConversationalRetrievalChain
+# from langchain.memory import ConversationBufferMemory
+# from langchain.chat_models import ChatOpenAI
+# from langchain.document_loaders import TextLoader
+# from langchain.document_loaders import PyPDFLoader
+# import panel as pn
+# import param
+# import re
+# import os
+
+# api_token = os.environ.get('HUGGINGFACEHUB_API_TOKEN')
+
+# model = HuggingFaceHub(
+#     huggingfacehub_api_token=api_token,
+#     repo_id="mistralai/Mistral-7B-Instruct-v0.2",
+#     task="conversational",
+#     model_kwargs={"temperature": 0.8, "max_length": 1000},
+# )
+# template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use three sentences maximum. Keep the answer as concise as possible. Always say "thanks for asking!" at the end of the answer. 
+# {context}
+# Question: {question}
+# Helpful Answer:"""
+# QA_CHAIN_PROMPT = PromptTemplate.from_template(template)
+
+# def load_db(file, k):
+#     # load documents
+#     loader = PyPDFLoader(file)
+#     documents = loader.load()
+#     # split documents
+#     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
+#     docs = text_splitter.split_documents(documents)
+#     # define embedding
+#     embeddings = HuggingFaceEmbeddings()
+#     # create vector database from data
+#     db = vectorstores.FAISS.from_documents(docs, embeddings)
+#     # define retriever
+#     retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k})
+#     # create a chatbot chain. Memory is managed externally.
+#     question_generator_chain = LLMChain(llm=model, prompt=QA_CHAIN_PROMPT)
+
+#     qa = ConversationalRetrievalChain.from_llm(
+#         llm=model,
+#         chain_type="stuff",
+#         retriever=retriever,
+#         return_source_documents=True,
+#         return_generated_question=True,
+#     )
+
+#     return qa
+
+# chat_history = []  # initialize chat history
+
+# def greet(question, pdf_file):
+#     global chat_history
+#     a = load_db(pdf_file, 3)
+#     r = a.invoke({"question": question, "chat_history": chat_history})
+#     match = re.search(r'Helpful Answer:(.*)', r['answer'])
+#     if match:
+#         helpful_answer = match.group(1).strip()
+#         # Extend chat history with the current question and answer
+#         chat_history.extend([(question, helpful_answer)])
+#         return helpful_answer
+#     else:
+#         return "No helpful answer found."
+
+# iface = gr.Interface(fn=greet, inputs=["text", "file"], outputs="text")
+# iface.launch(share=True)
+
+
+
 import gradio as gr
-import pandas as pd
+import os
+import re
 from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import Chroma, faiss
-from langchain_community.llms import HuggingFaceEndpoint, HuggingFaceHub
-from langchain.chains import LLMChain
-from langchain_community.document_loaders.csv_loader import CSVLoader
+from langchain.vectorstores import faiss
+from langchain_community.llms import HuggingFaceHub
+from langchain.chains import ConversationalRetrievalChain
 from langchain_community.document_loaders import PyPDFLoader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain_community.document_loaders import TextLoader
-from langchain_community import vectorstores
+from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.prompts import PromptTemplate
-from langchain.chains import RetrievalQA
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
-from langchain.vectorstores import DocArrayInMemorySearch
-from langchain.document_loaders import TextLoader
-from langchain.chains import RetrievalQA, ConversationalRetrievalChain
-from langchain.memory import ConversationBufferMemory
-from langchain.chat_models import ChatOpenAI
-from langchain.document_loaders import TextLoader
-from langchain.document_loaders import PyPDFLoader
-import panel as pn
-import param
-import re
-import os
 
 api_token = os.environ.get('HUGGINGFACEHUB_API_TOKEN')
 
@@ -34,6 +105,7 @@ model = HuggingFaceHub(
     task="conversational",
     model_kwargs={"temperature": 0.8, "max_length": 1000},
 )
+
 template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use three sentences maximum. Keep the answer as concise as possible. Always say "thanks for asking!" at the end of the answer. 
 {context}
 Question: {question}
@@ -41,21 +113,13 @@ Helpful Answer:"""
 QA_CHAIN_PROMPT = PromptTemplate.from_template(template)
 
 def load_db(file, k):
-    # load documents
     loader = PyPDFLoader(file)
     documents = loader.load()
-    # split documents
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
     docs = text_splitter.split_documents(documents)
-    # define embedding
     embeddings = HuggingFaceEmbeddings()
-    # create vector database from data
-    db = vectorstores.FAISS.from_documents(docs, embeddings)
-    # define retriever
+    db = faiss.FAISS.from_documents(docs, embeddings)
     retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k})
-    # create a chatbot chain. Memory is managed externally.
-    question_generator_chain = LLMChain(llm=model, prompt=QA_CHAIN_PROMPT)
-
     qa = ConversationalRetrievalChain.from_llm(
         llm=model,
         chain_type="stuff",
@@ -63,23 +127,27 @@ def load_db(file, k):
         return_source_documents=True,
         return_generated_question=True,
     )
-
     return qa
 
 chat_history = []  # initialize chat history
 
-def greet(question, pdf_file):
+def greet(conversation):
     global chat_history
-    a = load_db(pdf_file, 3)
-    r = a.invoke({"question": question, "chat_history": chat_history})
-    match = re.search(r'Helpful Answer:(.*)', r['answer'])
-    if match:
-        helpful_answer = match.group(1).strip()
-        # Extend chat history with the current question and answer
-        chat_history.extend([(question, helpful_answer)])
-        return helpful_answer
-    else:
-        return "No helpful answer found."
-
-iface = gr.Interface(fn=greet, inputs=["text", "file"], outputs="text")
+    user_input = conversation['user']
+    pdf_file = conversation['file'][0] if 'file' in conversation else None
+    
+    if user_input:
+        a = load_db(pdf_file, 3)
+        r = a.invoke({"question": user_input, "chat_history": chat_history})
+        match = re.search(r'Helpful Answer:(.*)', r['answer'])
+        if match:
+            helpful_answer = match.group(1).strip()
+            # Extend chat history with the current question and answer
+            chat_history.extend([(user_input, helpful_answer)])
+            return {"system": "", "user": user_input, "assistant": helpful_answer}
+        else:
+            return {"system": "", "user": user_input, "assistant": "No helpful answer found."}
+    return {"system": "", "user": "", "assistant": ""}
+
+iface = gr.Interface(fn=greet, inputs=gr.Chat(), outputs=gr.Chat())
 iface.launch(share=True)