Update app.py

app.py

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+import gradio as gr
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.llms import HuggingFacePipeline
+from langchain.chains import RetrievalQA
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# Load and process documents
+doc_loader = TextLoader("dataset.txt")
+docs = doc_loader.load()
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+split_docs = text_splitter.split_documents(docs)
+
+# Create vector database
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+vectordb = FAISS.from_documents(split_docs, embeddings)
+
+# Load model and create pipeline
+model_name = "01-ai/Yi-Coder-9B-Chat"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
+qa_pipeline = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=500,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+# Set up LangChain
+llm = HuggingFacePipeline(pipeline=qa_pipeline)
+retriever = vectordb.as_retriever(search_kwargs={"k": 5})
+qa_chain = RetrievalQA.from_chain_type(
+    retriever=retriever,
+    chain_type="stuff",
+    llm=llm,
+    return_source_documents=False
+)
+
+def preprocess_query(query):
+    if "script" in query or "code" in query.lower():
+        return f"Write a CPSL script: {query}"
+    return query
+
+def clean_response(response):
+    result = response.get("result", "")
+    if "Answer:" in result:
+        return result.split("Answer:")[1].strip()
+    return result.strip()
+
+def chatbot_response(user_input):
+    processed_query = preprocess_query(user_input)
+    raw_response = qa_chain.invoke({"query": processed_query})
+    return clean_response(raw_response)
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# CPSL Chatbot")
+    chat_history = gr.Chatbot()
+    user_input = gr.Textbox(label="Your Message:")
+    send_button = gr.Button("Send")
+
+    def interact(user_message, history):
+        bot_reply = chatbot_response(user_message)
+        history.append((user_message, bot_reply))
+        return history, history
+
+    send_button.click(interact, inputs=[user_input, chat_history], outputs=[chat_history, chat_history])