File size: 3,547 Bytes
744603c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | # Streamlit is an open-source framework for building interactive web apps in Python.
# Run this file with the SAME interpreter the notebook uses (has the matching
# LangChain 1.x versions):
# d:\2026\July\Agentic-RAG-with-LangGraph-and-Ollama\.venv\Scripts\python.exe -m streamlit run app.py
# (NOT `python app.py` — that skips Streamlit's runtime and breaks session state.)
import os
import time
import logging
import streamlit as st
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
# ---- Logging setup ---------------------------------------------------------
# Logs print to the terminal where you ran `streamlit run app.py`.
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s | %(levelname)-7s | %(message)s",
datefmt="%H:%M:%S",
)
log = logging.getLogger("langchain-demo")
# Load OPENAI_API_KEY and HUGGINGFACEHUB_API_TOKEN from the .env file
log.info("STEP 0: Loading environment variables from .env")
load_dotenv()
log.info(" OPENAI_API_KEY set: %s", bool(os.getenv("OPENAI_API_KEY")))
log.info(" HUGGINGFACEHUB_API_TOKEN set: %s", bool(os.getenv("HUGGINGFACEHUB_API_TOKEN")))
# ---- Answer function -------------------------------------------------------
# Same code shape as LLM_Intro.ipynb, with step-by-step logging to the terminal.
def load_answer(provider, question):
t0 = time.perf_counter()
log.info("STEP 1: Provider selected → %s", provider)
log.info("STEP 2: Question → %r", question)
if provider == "OpenAI":
log.info("STEP 3: Building ChatOpenAI(model='gpt-4o')")
llm = ChatOpenAI(model="gpt-4o")
log.info("STEP 4: Calling llm.invoke(...) — waiting for OpenAI")
answer = llm.invoke(question).content
else:
log.info("STEP 3: Building HuggingFaceEndpoint(repo_id='Qwen/Qwen2.5-72B-Instruct', "
"task='conversational', provider='novita')")
llm = HuggingFaceEndpoint(
repo_id="Qwen/Qwen2.5-72B-Instruct",
task="conversational",
provider="novita",
huggingfacehub_api_token=os.getenv("HUGGINGFACEHUB_API_TOKEN"),
)
log.info("STEP 4: Wrapping in ChatHuggingFace and calling chat.invoke(...) — waiting for HF")
chat = ChatHuggingFace(llm=llm)
answer = chat.invoke(question).content
elapsed = time.perf_counter() - t0
log.info("STEP 5: Got response (%d chars) in %.2fs", len(answer), elapsed)
return answer
# ---- App UI ----------------------------------------------------------------
st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
st.header("HuggingFace Demo")
# Sidebar: let the user choose the provider.
with st.sidebar:
st.subheader("Model settings")
provider = st.selectbox("Provider", ["OpenAI", "HuggingFace"])
# Gets the user input
def get_text():
input_text = st.text_input("You: ", key="input")
return input_text
user_input = get_text()
submit = st.button("Generate")
# Only call the LLM when the button is clicked and there is input
if submit and user_input:
with st.spinner(f"Asking {provider}…"):
try:
response = load_answer(provider, user_input)
st.subheader("Answer:")
st.write(response)
except Exception as e:
log.exception("ERROR during load_answer")
st.error(f"{type(e).__name__}: {e}")
elif submit:
st.warning("Please enter a question first.")
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