Upload app.py

app.py

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+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 = os.getenv('CLAUDE_API_KEY')
+
+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()