Delete app.py

app.py

@@ -1,159 +0,0 @@
-import os
-import gradio as gr
-import faiss
-import numpy as np
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain.chains import ConversationalRetrievalChain
-from langchain.memory import ConversationBufferMemory
-from langchain_core.documents import Document
-from PyPDF2 import PdfReader
-from langchain_anthropic import ChatAnthropic
-
-API_KEY = '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'
-llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0.5, max_tokens=8192, anthropic_api_key=API_KEY)
-
-embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
-
-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-
-vector_store = None
-
-
-def process_file(file_path):
-    _, ext = os.path.splitext(file_path)
-    try:
-        if ext.lower() == '.txt':
-            with open(file_path, 'r', encoding='utf-8') as file:
-                text = file.read()
-        elif ext.lower() == '.docx':
-            with open(file_path, 'rb') as file:
-                content = file.read()
-                text = content.decode('utf-8', errors='ignore')
-        elif ext.lower() == '.pdf':
-            with open(file_path, 'rb') as file:
-                pdf_reader = PdfReader(file)
-                text = '\n'.join([page.extract_text() for page in pdf_reader.pages if page.extract_text()])
-        else:
-            print(f"Unsupported file type: {ext}")
-            return None
-
-        return [Document(page_content=text, metadata={"source": file_path})]
-    except Exception as e:
-        print(f"Error processing file {file_path}: {str(e)}")
-        return None
-
-
-def process_files(file_list, progress=gr.Progress()):
-    global vector_store
-    documents = []
-    total_files = len(file_list)
-
-    for i, file in enumerate(file_list):
-        progress((i + 1) / total_files, f"Processing file {i + 1} of {total_files}")
-        if file.name.lower().endswith(('.txt', '.docx', '.pdf')):
-            docs = process_file(file.name)
-            if docs:
-                documents.extend(docs)
-
-    if not documents:
-        return "No documents were successfully processed. Please check your files and try again."
-
-    progress(0.5, "Splitting text")
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=200)
-    texts = text_splitter.split_documents(documents)
-
-    progress(0.7, "Creating embeddings")
-    vector_store = FAISS.from_documents(texts, embeddings)
-
-    progress(0.9, "Saving vector store")
-    vector_store.save_local("faiss_index")
-
-    progress(1.0, "Completed")
-    return f"Embedding process completed and database created. Processed {len(documents)} files. You can now start chatting!"
-
-
-def load_existing_index(folder_path):
-    global vector_store
-    try:
-        index_file = os.path.join(folder_path, "index.faiss")
-        pkl_file = os.path.join(folder_path, "index.pkl")
-
-        if not os.path.exists(index_file) or not os.path.exists(pkl_file):
-            return f"Error: FAISS index files not found in {folder_path}. Please ensure both 'index.faiss' and 'index.pkl' are present."
-
-        vector_store = FAISS.load_local(folder_path, embeddings, allow_dangerous_deserialization=True)
-        return f"Successfully loaded existing index from {folder_path}."
-    except Exception as e:
-        return f"Error loading index: {str(e)}"
-
-
-def chat(message, history):
-    global vector_store
-    if vector_store is None:
-        return "Please load documents or an existing index first."
-
-    qa_chain = ConversationalRetrievalChain.from_llm(
-        llm,
-        vector_store.as_retriever(),
-        memory=memory
-    )
-
-    result = qa_chain.invoke({"question": message, "chat_history": history})
-    return result['answer']
-
-
-def reset_chat():
-    global memory
-    memory.clear()
-    return []
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Document-based Chatbot")
-
-    with gr.Row():
-        with gr.Column():
-            file_input = gr.File(label="Select Files", file_count="multiple", file_types=[".pdf", ".docx", ".txt"])
-            process_button = gr.Button("Process Files")
-        with gr.Column():
-            index_folder = gr.Textbox(label="Existing Index Folder Path",
-                                      value="C:\\Works\\Data\\projects\\Python\\QA_Chatbot\\faiss_index")
-            load_index_button = gr.Button("Load Existing Index")
-
-    output = gr.Textbox(label="Processing Output")
-
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox()
-    send = gr.Button("Send")
-    clear = gr.Button("Clear")
-
-
-    def process_selected_files(files):
-        if files:
-            return process_files(files)
-        else:
-            return "No files selected. Please select files and try again."
-
-
-    def load_selected_index(folder_path):
-        return load_existing_index(folder_path)
-
-
-    process_button.click(process_selected_files, file_input, output)
-    load_index_button.click(load_selected_index, index_folder, output)
-
-
-    def respond(message, chat_history):
-        bot_message = chat(message, chat_history)
-        chat_history.append((message, bot_message))
-        return "", chat_history
-
-
-    msg.submit(respond, [msg, chatbot], [msg, chatbot])
-    send.click(respond, [msg, chatbot], [msg, chatbot])
-    clear.click(reset_chat, None, chatbot)
-
-if __name__ == "__main__":
-    demo.launch()