Update app.py

app.py

@@ -1,87 +1,22 @@
 import gradio as gr
-import torch
-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
 import spaces
-zero = torch.Tensor([0]).cuda()
-print(zero.device)  # This will likely print 'cpu'
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# 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)
-
-# Load model and tokenizer
-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=torch.float16 if device == "cuda" else torch.float32
-)
-
-# 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
-)
-
-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()
-
-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])
+import torch
 
-# Launch the interface
-if __name__ == "__main__":
-    chat_interface.launch()
+def gpu_info():
+    info = []
+    info.append(f"Torch Version: {torch.__version__}")
+    info.append(f"CUDA Available: {torch.cuda.is_available()}")
+    info.append(f"GPU Count: {torch.cuda.device_count()}")
+    
+    if torch.cuda.is_available():
+        info.append(f"GPU Name: {torch.cuda.get_device_name(0)}")
+        info.append(f"VRAM: {torch.cuda.get_device_properties(0).total_memory // (1024**2)} MB")
+    else:
+        info.append("No GPU detected.")
+    
+    return "\n".join(info)
+
+# Gradio Interface
+iface = gr.Interface(fn=gpu_info, inputs=None, outputs="text", title="GPU Information Checker")
+
+iface.launch()