| import os |
|
|
| import gradio as gr |
| from dotenv import load_dotenv |
| from langchain_chroma import Chroma |
| from langchain_community.tools import DuckDuckGoSearchRun |
| from langchain_core.messages import HumanMessage, SystemMessage |
| from langchain_huggingface import HuggingFaceEmbeddings |
| from langchain_openai import ChatOpenAI |
|
|
| from news_price_correlation import build_correlation_analysis |
| from nvidia_agent_core import forecast_markdown, predict_nvidia_stock_payload, route_query |
|
|
|
|
| load_dotenv() |
|
|
| if not os.getenv("OPENAI_API_KEY"): |
| raise ValueError("OPENAI_API_KEY was not found. Add it to .env or your environment.") |
|
|
| llm = ChatOpenAI(model="gpt-5.4", temperature=0.3, max_tokens=1024) |
| ddg_search = DuckDuckGoSearchRun() |
|
|
| vectorstore = None |
| try: |
| import chromadb |
| from chromadb.config import Settings |
|
|
| embeddings = HuggingFaceEmbeddings(model_name="all-mpnet-base-v2", model_kwargs={"device": "cpu"}) |
| client = chromadb.PersistentClient(path="./chroma_db_v2", settings=Settings(allow_reset=True)) |
| vectorstore = Chroma( |
| client=client, |
| collection_name="nvidia_annual_reports_2014_2025", |
| embedding_function=embeddings, |
| ) |
| except Exception as exc: |
| print(f"RAG unavailable; Gradio app will continue without annual-report retrieval: {exc}") |
|
|
|
|
| def forecast_answer() -> str: |
| payload = predict_nvidia_stock_payload(periods=7) |
| return forecast_markdown(payload) |
|
|
|
|
| def correlation_answer(query: str) -> str: |
| return build_correlation_analysis( |
| query=query, |
| search=ddg_search.run, |
| llm=llm, |
| csv_path="nvda_2014_to_2026.csv", |
| ) |
|
|
|
|
| def researcher_answer(query: str) -> str: |
| context = "" |
| if vectorstore: |
| docs = vectorstore.similarity_search(query, k=5) |
| context = "\n\n".join(doc.page_content[:700] for doc in docs) |
|
|
| news = ddg_search.run(f"NVIDIA {query} latest news OR earnings OR Blackwell OR Huawei")[:1000] |
| response = llm.invoke( |
| [ |
| SystemMessage(content="You are NVIDIA's expert financial and strategy analyst."), |
| HumanMessage(content=f"Question: {query}\n\nAnnual-report context:\n{context}\n\nNews:\n{news}"), |
| ] |
| ) |
| return response.content |
|
|
|
|
| def get_response(query: str) -> str: |
| route = route_query(query) |
| if route == "ML_agent": |
| return forecast_answer() |
| if route == "correlation_agent": |
| return correlation_answer(query) |
| if route in {"results_strategy_agent", "outlook_agent"}: |
| return researcher_answer(query) |
|
|
| response = llm.invoke( |
| [ |
| SystemMessage(content="You are a helpful NVIDIA assistant."), |
| HumanMessage(content=query), |
| ] |
| ) |
| return response.content |
|
|
|
|
| def chat(message, history): |
| try: |
| return get_response(message) |
| except Exception as exc: |
| return f"Error: {exc}" |
|
|
|
|
| demo = gr.ChatInterface( |
| fn=chat, |
| title="NVIDIA AI Assistant", |
| description="Notebook-aligned multi-agent assistant with RAG, news correlation, and Prophet v3 residual ML forecasting.", |
| examples=[ |
| "Predict Nvidia stock price for the next 7 business days", |
| "What did Nvidia report about gross profit in the 2025 annual report?", |
| "Why did NVDA sell off on China/Huawei export-control news?", |
| "If CCP China invades Taiwan, predict the scale of NVDA selloff and share price drop", |
| ], |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| demo.launch(server_name="0.0.0.0", server_port=7860, share=False) |
|
|
