Create app.py

app.py

@@ -0,0 +1,116 @@
+import os
+import gradio as gr
+from langchain.chains import RetrievalQA
+from langchain_community.document_loaders import TextLoader
+from langchain_community.document_loaders import UnstructuredExcelLoader
+from langchain.indexes import VectorstoreIndexCreator
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+
+from dotenv import find_dotenv, load_dotenv 
+
+from langchain.chains import create_retrieval_chain, RetrievalQA
+from langchain_community.vectorstores import FAISS
+
+_=load_dotenv(find_dotenv())
+hf_api = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+def indexdocs (file_path, progress=gr.Progress()):
+    
+    progress(0.1, desc="Loading documents...")
+    
+    loaders = [UnstructuredExcelLoader(file, mode="elements") for file in file_path]
+    documents=[]
+    for loader in loaders:
+        documents.extend(loader.load())
+    
+    progress(0.3, desc="Splitting documents...")
+    
+    text_splitter = RecursiveCharacterTextSplitter (chunk_size=1500, chunk_overlap=300)
+    pages=text_splitter.split_documents(documents)
+    embedding = HuggingFaceEmbeddings()
+    
+    progress(0.5, desc="Creating vectorstore...")
+    
+    vector=FAISS.from_documents (documents=pages,embedding=embedding)
+    retriever = vector.as_retriever()
+    
+    progress(0.8, desc="Setting up language model...")
+    
+    memory = ConversationBufferMemory(
+            memory_key="chat_history",
+            output_key='answer',
+            return_messages=True
+        )
+    
+    llm = HuggingFaceEndpoint(
+                repo_id="Mistralai/Mistral-7B-Instruct-v0.2", 
+                temperature = 0.1, 
+                max_new_tokens = 200, 
+                top_k = 1 #top_k,
+            )
+    
+    qa_chain = ConversationalRetrievalChain.from_llm(
+            llm,
+            retriever=retriever,
+            chain_type="stuff", 
+            memory=memory,
+            return_source_documents=True,
+            verbose=False,
+        )
+    return qa_chain, None 
+
+def format_chat_history(chat_history):
+    formatted_chat_history = []
+    for user_message, bot_message in chat_history:
+        formatted_chat_history.append(f"User: {user_message}")
+        formatted_chat_history.append(f"Assistant: {bot_message}")
+    return formatted_chat_history
+
+def chat(qa_chain,msg,history):
+    formatted_chat_history = format_chat_history(history)
+    response = qa_chain.invoke({"question": msg, "chat_history": formatted_chat_history})
+    response_answer = response["answer"]
+    response_sources=response["source_documents"]
+    print (response_sources[0])
+    response_source1= response_sources[0].metadata["filename"]
+    response_source_sheet= response_sources[0].metadata["page_name"]
+    new_history = history + [(msg, response_answer)]
+    return qa_chain, gr.update(value=""), new_history, response_source1, response_source_sheet
+
+with gr.Blocks() as demo:
+    qa_chain=gr.State()
+    
+    gr.Markdown(
+        """
+        # MS Excel Knowledge Base QA using RAG
+        """
+    )
+    with gr.Column():
+            file_list = gr.File(label='Upload your MS Excel files...', file_count='multiple', file_types=['.xls,.xlsx'])
+            fileuploadbtn= gr.Button ("Index Documents and Start Chatting")
+    with gr.Row():
+        chatbot=gr.Chatbot(height=300)
+    with gr.Row():
+        source=gr.Textbox(info="Source",container=False,scale=4)
+        source_page=gr.Textbox(info="Sheet",container=False,scale=1)
+    with gr.Row():
+        prompt=gr.Textbox(placeholder="Please enter your prompt...",container=False, scale=4, visible=True, interactive=False)
+        promptsubmit=gr.Button("Submit", scale=1, visible=True, interactive=False)
+    gr.Markdown(
+        """
+        # Responsible AI Usage
+        Your documents uploaded to the system or interactions with the chatbot are not saved.
+        """
+    )
+
+    fileuploadbtn.click(fn=indexdocs, inputs = [file_list], outputs=[qa_chain,chatbot]).then(lambda:[gr.Textbox(interactive=True), gr.Button (interactive=True)], \
+    inputs=None, outputs=[prompt,promptsubmit], queue=False)  
+    promptsubmit.click(fn=chat, inputs=[qa_chain,prompt,chatbot], outputs=[qa_chain,prompt,chatbot,source,source_page],queue=False)
+    prompt.submit(fn=chat, inputs=[qa_chain,prompt,chatbot], outputs=[qa_chain,prompt,chatbot,source,source_page],queue=False)
+
+if __name__ == "__main__":
+    demo.launch()