Update app.py

app.py

@@ -1,14 +1,93 @@
 import gradio as gr
-import spaces
 import torch
+import spaces
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.document_loaders import TextLoader
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains import RetrievalQA
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from langchain_huggingface import HuggingFacePipeline
+
+# Load and process the document
+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 embeddings and vector store
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+vectordb = FAISS.from_documents(split_docs, embeddings)
 
-zero = torch.Tensor([0]).cuda()
-print(zero.device) # <-- 'cpu' 🤔
+# Load model and tokenizer
+model_name = "01-ai/Yi-Coder-9B-Chat"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 @spaces.GPU
-def greet(n):
-    print(zero.device) # <-- 'cuda:0' 🤗
-    return f"Hello {zero + n} Tensor"
+def setup_model():
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name, 
+        device_map="auto", 
+        torch_dtype=torch.float16 if device.type == "cuda" else torch.float32
+    )
+    return model, device
+
+model, device = setup_model()
+
+# Set up the QA pipeline
+qa_pipeline = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=750,
+    pad_token_id=tokenizer.eos_token_id,
+    device=device
+)
+
+llm = HuggingFacePipeline(pipeline=qa_pipeline)
+
+# Set up the retriever and QA chain
+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()
+
+@spaces.GPU
+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 chat_interface:
+    gr.Markdown("# CPSL Chatbot")
+    chat_history = gr.Chatbot(type='messages')
+    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({"role": "user", "content": user_message})
+        history.append({"role": "assistant", "content": bot_reply})
+        return history, history
+
+    send_button.click(interact, inputs=[user_input, chat_history], outputs=[chat_history, chat_history])
 
-demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
-demo.launch()
+# Launch the interface
+if __name__ == "__main__":
+    chat_interface.launch()