app

app.py

@@ -1,24 +1,33 @@
 import gradio as gr
 import pandas as pd
 from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import faiss
-from langchain_community.llms import HuggingFaceHub
-from langchain.chains import ConversationalRetrievalChain, LLMChain
+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 RecursiveCharacterTextSplitter
-from langchain import vectorstores
+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
-import os
+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')
 
-memory = ConversationBufferMemory(
-    memory_key="chat_history",
-    return_messages=True
-)
-
 model = HuggingFaceHub(
     huggingfacehub_api_token=api_token,
     repo_id="mistralai/Mistral-7B-Instruct-v0.2",
@@ -53,34 +62,24 @@ def load_db(file, k):
         retriever=retriever,
         return_source_documents=True,
         return_generated_question=True,
-        memory=memory,
     )
 
     return qa
 
-def chat(input_text, pdf_file):
-    qa = load_db(pdf_file, 3)
-    print("MEMORY")
-    print(memory)
-    if not memory.history:
-        # If no previous conversation, start with a greeting
-        response = qa.invoke({"question": "Hi, how can I help you today?", "chat_history": []})
-        memory.update(response["chat_history"])
+chat_history = []  # initialize chat history
 
-    response = qa.invoke({"question": input_text, "chat_history": memory.history})
-    
-    # Extracting the helpful answer from the response
-    match = re.search(r'Helpful Answer:(.*)', response['answer'])
+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:
-        helpful_answer = "No helpful answer found."
-
-    # Update the chat history
-    memory.update([(input_text, helpful_answer)])
-
-    return helpful_answer
-
+        return "No helpful answer found."
 
-iface = gr.Interface(fn=chat, inputs=["text", "file"], outputs="text")
+iface = gr.Interface(fn=greet, inputs=["text", "file"], outputs="text")
 iface.launch(share=True)