change GPU duration

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+test.py

app.py

@@ -120,7 +120,7 @@ conversational_rag_chain = RunnableWithMessageHistory(
     output_messages_key="answer",
 )
 
-@spaces.GPU(duration=5)
+@spaces.GPU(duration=10)
 def handle_message(question, history={}):
     response = ''
     chain = conversational_rag_chain.pick("answer")

temp.py

@@ -1,176 +0,0 @@
-import os
-from dotenv import load_dotenv
-load_dotenv(".env")
-
-os.environ['USER_AGENT'] = os.getenv("USER_AGENT")
-os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
-os.environ["TOKENIZERS_PARALLELISM"]='true'
-
-from langchain.chains import create_history_aware_retriever, create_retrieval_chain
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_community.chat_message_histories import ChatMessageHistory
-from langchain_community.document_loaders import WebBaseLoader
-from langchain_core.chat_history import BaseChatMessageHistory
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain_core.runnables.history import RunnableWithMessageHistory
-
-from pinecone import Pinecone
-from pinecone_text.sparse import BM25Encoder
-
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.retrievers import PineconeHybridSearchRetriever
-
-from langchain_groq import ChatGroq
-
-# from flask import Flask, request, render_template
-# from flask_cors import CORS
-# from flask_socketio import SocketIO, emit
-
-import gradio as gr
-import spaces
-import torch
-
-zero = torch.Tensor([0]).cuda()
-print(zero.device) # <-- 'cpu' 🤔
-
-@spaces.GPU
-def greet(n):
-    print(zero.device) # <-- 'cuda:0' 🤗
-    return f"Hello {zero + n} Tensor"
-
-
-# app = Flask(__name__)
-# CORS(app)
-# socketio = SocketIO(app, cors_allowed_origins="*")
-# app.config['SESSION_COOKIE_SECURE'] = True  # Use HTTPS
-# app.config['SESSION_COOKIE_HTTPONLY'] = True
-# app.config['SESSION_COOKIE_SAMESITE'] = 'Lax'
-# app.config['SECRET_KEY'] = os.getenv('SECRET_KEY')
-
-try:
-    pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
-    index_name = "traveler-demo-website-vectorstore"
-    # connect to index
-    pinecone_index = pc.Index(index_name)
-except:
-    pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
-    index_name = "traveler-demo-website-vectorstore"
-    # connect to index
-    pinecone_index = pc.Index(index_name)
-
-bm25 = BM25Encoder().load("./bm25_traveler_website.json")
-
-embed_model = HuggingFaceEmbeddings(model_name="Alibaba-NLP/gte-large-en-v1.5", model_kwargs={"trust_remote_code":True})
-
-retriever = PineconeHybridSearchRetriever(
-    embeddings=embed_model, 
-    sparse_encoder=bm25, 
-    index=pinecone_index, 
-    top_k=20, 
-    alpha=0.5, 
-)
-
-llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0.1, max_tokens=1024, max_retries=2)
-
-### Contextualize question ###
-contextualize_q_system_prompt = """Given a chat history and the latest user question \
-which might reference context in the chat history, formulate a standalone question \
-which can be understood without the chat history. Do NOT answer the question, \
-just reformulate it if needed and otherwise return it as is.
-"""
-contextualize_q_prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", contextualize_q_system_prompt),
-        MessagesPlaceholder("chat_history"),
-        ("human", "{input}")
-    ]
-)
-
-history_aware_retriever = create_history_aware_retriever(
-    llm, retriever, contextualize_q_prompt
-)
-
-
-qa_system_prompt = """You are a highly skilled information retrieval assistant. Use the following pieces of retrieved context to answer the question. \
-Provide links to sources provided in the answer. \
-If you don't know the answer, just say that you don't know. \
-Do not give extra long answers. \
-When responding to queries, your responses should be comprehensive and well-organized. For each response: \
-    1. Provide Clear Answers \
-    2. Include Detailed References: \
-        - Include links to sources and any links or sites where there is a mentioned in the answer.
-        - Links to Sources: Provide URLs to credible sources where users can verify the information or explore further. \
-        - Downloadable Materials: Include links to any relevant downloadable resources if applicable. \
-        - Reference Sites: Mention specific websites or platforms that offer additional information. \
-    3. Formatting for Readability: \
-        - Bullet Points or Lists: Where applicable, use bullet points or numbered lists to present information clearly. \
-        - Emphasize Important Information: Use bold or italics to highlight key details. \
-    4. Organize Content Logically \
-Do not include anything about context in the answer. \
-{context}
-"""
-qa_prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", qa_system_prompt),
-        MessagesPlaceholder("chat_history"),
-        ("human", "{input}")
-    ]
-)
-question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
-
-rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
-
-### Statefully manage chat history ###
-store = {}
-
-def clean_temporary_data():
-    store = {}
-
-def get_session_history(session_id: str) -> BaseChatMessageHistory:
-    if session_id not in store:
-        store[session_id] = ChatMessageHistory()
-    return store[session_id]
-
-
-conversational_rag_chain = RunnableWithMessageHistory(
-    rag_chain,
-    get_session_history,
-    input_messages_key="input",
-    history_messages_key="chat_history",
-    output_messages_key="answer",
-)
-
-# Stream response to client
-@socketio.on('message')
-def handle_message(data):
-    question = data.get('question')
-    session_id = data.get('session_id', 'abc123')
-    chain = conversational_rag_chain.pick("answer")
-    
-    try:
-        for chunk in chain.stream(
-                {"input": question},
-                config={
-                    "configurable": {"session_id": "abc123"}
-                },
-            ):
-                emit('response', chunk, room=request.sid)
-    except:
-        for chunk in chain.stream(
-                {"input": question},
-                config={
-                    "configurable": {"session_id": "abc123"}
-                },
-            ):
-                emit('response', chunk, room=request.sid)
-
-@app.route("/")
-def index_view():
-    return render_template('chat.html') 
-
-if __name__ == '__main__': 
-    socketio.run(app, debug=True)
-
-
-    demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
-    demo.launch()

test.py

@@ -0,0 +1,16 @@
+from gradio_client import Client
+import timeit
+
+client = Client("Ritesh-hf/rag-api")
+
+
+while True:
+    question = input("Question: ")
+    start_time = timeit.default_timer()
+    result = client.predict(
+            question=question,
+            api_name="/chat"
+    )
+    end_time = timeit.default_timer()
+    print(result)
+    print("Time Taken: ", end_time-start_time)