init

.gitignore

@@ -0,0 +1,30 @@
+# Ignore ChromaDB storage folder
+**/chroma_storage/*
+
+# Python artifacts
+__pycache__/
+*.py[cod]
+*.pkl
+*.db
+*.sqlite3
+
+# Environment files
+.env
+*.env
+
+# OS-specific
+.DS_Store
+Thumbs.db
+
+# Jupyter/IPython
+.ipynb_checkpoints/
+
+# Virtual environments
+.venv/
+.env/
+
+# Logs and cache
+*.log
+*.cache/
+.cache/
+

Dockerfile

@@ -0,0 +1,33 @@
+# Base Python image
+FROM python:3.10-slim
+
+# Set environment variables
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+# Create a user to avoid root-level issues with ChromaDB
+RUN adduser --disabled-password --gecos "" appuser
+USER appuser
+
+# Create working directory
+WORKDIR /home/appuser/app
+
+# Copy project files
+COPY --chown=appuser:appuser . .
+
+# Install Python dependencies
+RUN pip install --upgrade pip \
+ && pip install -r requirement.txt
+
+# If using .env file, install python-dotenv and make sure app reads it
+RUN pip install python-dotenv
+
+# Expose FastAPI default port
+EXPOSE 8000
+# Expose Streamlit default port
+EXPOSE 8501
+
+# Start FastAPI app in the background, then Streamlit
+CMD uvicorn streamlit_app.main_api:app --host 0.0.0.0 --port 8000 & \
+    streamlit run streamlit_app/app.py --server.port 8501
+

README.md

@@ -1,10 +1,31 @@
+## Market Brief Agent
+
+### Workflow Diagram
 ---
-title: RagaAI
-emoji: 🌍
-colorFrom: blue
-colorTo: yellow
-sdk: docker
-pinned: false
----
+![](diagram.jpeg)
+
+#### Overview
+* An acyclic workflow where the user interacts through a Streamlit App.
+* User's query is first parsed by the Orchestrator API endpoint that returns the result for what tools to use along with the result of those tools' execution.
+* The original user query and the generated supporting context are then passed to the final response synthesizer.
+* Final response is streamed back to the Streamlit app again via API communication.
+* User can further instruct to listen to the generated response using Deepgram's voice models.
+
+### Tools 
+
+**All tools are accessible through an API interface**
+* `/data/get_historical_data` : This tool brings historical changes in a particular given stock. Must provide a YFinance ticker as a parameter.
+* `/data/get_earning_metrics` : This tool generates the stock earnings summary over the past 3–4 years using YFinance earning metrics.
+* `/data/get_portfolio_data` : This tool brings a current portfolio snapshot/updates. *Currently only supports IND portfolio*.
+* `/data/get_portfolio_data` : This is a ***RAG*** based tool. It uses a company's prior documents as a knowledge base and uses semantic similarity to provide context on company-related user queries.
+* `/data/get_portfolio_data` : Tool to make orchestration decisions—i.e., which tool to call with what parameters.
+* `/data/get_portfolio_data` : Tool to generate the final user-friendly response with **guardrails** to avoid giving aggressive financial advice.
+
+### Deployment
+
+Fully functional **Docker**-based deployment for maintainability and scalability.
+
+```DOCKER FILE CODE```
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+#### FYIs
+* Voice I/O is slow because of Streamlit voice processing and Deepgram API latency.

data_ingetion/data_api.py

@@ -0,0 +1,45 @@
+from fastapi import APIRouter
+from pydantic import BaseModel
+from .market_data import price_change, earning_summary, portfolio_data
+from .vectroDB import get_relevant_chunks
+
+app = APIRouter()
+
+
+class HistoricalData(BaseModel):
+    symbol: str
+    period: int
+
+
+class EarningReq(BaseModel):
+    symbol: str
+
+
+class PortfolioReq(BaseModel):
+    region: str
+
+
+class KnowledgeReq(BaseModel):
+    query: str
+
+
+@app.post("/get_historical_data")
+def get_historical_data(req: HistoricalData):
+    symbol = req.symbol
+    period = req.period
+    return {"response": price_change(symbol, period)}
+
+
+@app.post("/get_earning_metrics")
+def get_eraning_metrics(req: EarningReq):
+    return {"response": earning_summary(req.symbol)}
+
+
+@app.post("/get_portfolio_data")
+def get_portfolio_data(req: PortfolioReq):
+    return {"response": portfolio_data(req.region)}
+
+
+@app.post("/get_knowledge")
+def get_knowledge(req: KnowledgeReq):
+    return {"response": get_relevant_chunks(req.query)}

data_ingetion/market_data.py

@@ -0,0 +1,84 @@
+import yfinance as yf
+import datetime
+import pandas as pd
+import os
+
+
+def portfolio_data(region: str = "IND"):
+    if region == "IND":
+        portfolio = "/data_ingetion/portfolios/IND.csv"
+    else:
+        portfolio = "/data_ingetion/portfolios/US.csv"
+
+    pc_file = os.getcwd() + "/data_ingetion/portfolios/portfolio_change.csv"
+
+    today = datetime.datetime.today().strftime("%Y-%m-%d")
+
+    portfolio_change = pd.read_csv(pc_file)
+    df = pd.read_csv(os.getcwd() + portfolio)
+
+    if today not in portfolio_change.columns:
+        pc = []
+
+        for ticker in df["Ticker Symbol"]:
+            pc.append(price_change(ticker, 7, True))
+
+        portfolio_change[today] = pc
+
+        portfolio_change.to_csv(pc_file, index=False)
+
+    df["Price Change%"] = portfolio_change[today]
+    df.drop("Date of Investment", axis=1, inplace=True)
+
+    return f"Portfolio and change in prices in part 7 days : \n {str(df)}"
+
+
+def price_change(symbol, days: int, raw: bool = False):
+    stock = yf.Ticker(ticker=symbol)
+
+    today_price = stock.history(period="1d")
+
+    # Target date: 1 year and 7 days ago
+    target_date = datetime.datetime.today() - datetime.timedelta(days=days)
+    start_date = (target_date - datetime.timedelta(days=5)).strftime("%Y-%m-%d")
+    end_date = (target_date + datetime.timedelta(days=1)).strftime("%Y-%m-%d")
+
+    # Fetch range around the target date
+    history = stock.history(start=start_date, end=end_date)
+
+    # Get the latest available price before or on the target date
+    past_price = history[history.index <= target_date.strftime("%Y-%m-%d")].iloc[-1]
+
+    percentage_difference = (
+        (today_price["Close"] - past_price["Close"]) / past_price["Close"]
+    ).values[0] * 100
+
+    response = (
+        f"Price change for {symbol} in past {days} days is {percentage_difference:.2f}%"
+    )
+    if raw:
+        return percentage_difference
+    return response
+
+
+def earning_summary(symbol):
+    stock = yf.Ticker(ticker=symbol)
+    metrics = [
+        "EBITDA",
+        "Total Expenses",
+        "Basic EPS",
+        "Net Income",
+        "Gross Profit",
+        "Total Revenue",
+    ]
+    currency = stock.fast_info.currency
+    income_metrics = stock.income_stmt
+
+    scaler = 1e7 if currency == "INR" else 1e6
+    units = "carore" if currency == "INR" else "millions"
+
+    selected_metric = income_metrics.loc[metrics] / scaler
+
+    response = f"Earning metrics for {symbol} are following in {currency} currency in {units}: \n {selected_metric}"
+
+    return response

data_ingetion/portfolios/IND.csv

@@ -0,0 +1,16 @@
+Stock Name,Ticker Symbol,Sector,Investment Percentage,Date of Investment
+Reliance Industries Ltd,RELIANCE.NS,Energy,12.5,2023-03-15
+Tata Consultancy Services,TCS.NS,IT,10.8,2023-01-20
+HDFC Bank Ltd,HDFCBANK.NS,Bank,9.2,2022-11-10
+Infosys Ltd,INFY.NS,IT,8.7,2023-02-28
+ICICI Bank Ltd,ICICIBANK.NS,Bank,7.9,2023-04-12
+Hindustan Unilever Ltd,HINDUNILVR.NS,FMCG,6.8,2022-12-05
+State Bank of India,SBIN.NS,Bank,6.5,2023-05-18
+Bharti Airtel Ltd,BHARTIARTL.NS,Telecom,5.9,2023-03-22
+ITC Ltd,ITC.NS,FMCG,5.4,2022-10-30
+Larsen & Toubro Ltd,LT.NS,Infrastructure,5.2,2023-01-08
+Wipro Ltd,WIPRO.NS,IT,4.8,2023-02-14
+Mahindra & Mahindra,M&M.NS,Auto,4.3,2023-04-05
+Dr Reddy's Laboratories,DRREDDY.NS,Pharma,4.1,2022-09-25
+Asian Paints Ltd,ASIANPAINT.NS,Paints,3.9,2023-06-02
+Bajaj Finance Ltd,BAJFINANCE.NS,NBFC,4.0,2023-03-08

data_ingetion/portfolios/US.csv

@@ -0,0 +1,16 @@
+Stock Name,Ticker Symbol,Sector,Investment Percentage,Date of Investment
+Apple Inc,AAPL,Technology,11.8,2023-02-15
+Microsoft Corporation,MSFT,Technology,10.5,2023-01-12
+Amazon.com Inc,AMZN,Consumer Discretionary,9.3,2022-12-08
+Alphabet Inc Class A,GOOGL,Technology,8.7,2023-03-22
+Tesla Inc,TSLA,Auto,7.9,2023-04-18
+JPMorgan Chase & Co,JPM,Bank,7.2,2022-11-25
+Johnson & Johnson,JNJ,Healthcare,6.8,2023-01-30
+Berkshire Hathaway Inc Class B,BRK-B,Financial Services,6.5,2022-10-15
+UnitedHealth Group Inc,UNH,Healthcare,5.9,2023-05-10
+Procter & Gamble Co,PG,Consumer Staples,5.4,2023-02-28
+Visa Inc Class A,V,Financial Services,5.1,2023-03-15
+Coca-Cola Company,KO,Consumer Staples,4.8,2022-09-20
+Home Depot Inc,HD,Consumer Discretionary,4.2,2023-04-05
+Mastercard Inc Class A,MA,Financial Services,3.9,2023-06-12
+Walt Disney Company,DIS,Entertainment,2.0,2023-01-25

data_ingetion/portfolios/portfolio_change.csv

@@ -0,0 +1,16 @@
+Stock Name,2025-05-27,2025-05-28
+Reliance Industries Ltd,-0.175487859247769,-1.1404930950667433
+Tata Consultancy Services,0.0114313334524853,-0.7969850038883989
+HDFC Bank Ltd,0.595332616257644,-0.17127095414659563
+Infosys Ltd,0.608740237640537,0.2103952074035703
+ICICI Bank Ltd,0.570162252856838,0.6298953390888131
+Hindustan Unilever Ltd,1.67014872857291,-0.08037152257869686
+State Bank of India,1.02462770226125,1.2006114216934662
+Bharti Airtel Ltd,1.92935065194481,1.843620059585048
+ITC Ltd,-0.229937919989491,-1.1680789821939643
+Larsen & Toubro Ltd,2.02365463825782,1.5965463591007245
+Wipro Ltd,-0.584680899688634,-1.3908837552335025
+Mahindra & Mahindra,-0.773623344928187,-3.0057884560591495
+Dr Reddy's Laboratories,1.55203404327962,1.461584480095272
+Asian Paints Ltd,1.3939710157998,-0.3978529476043621
+Bajaj Finance Ltd,1.01332745897125,0.7610763794509378

data_ingetion/vectroDB.py

@@ -0,0 +1,83 @@
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+import chromadb
+from openai import OpenAI
+import pypdf
+import uuid
+import os
+
+VECTOR_NAME = "database"
+EMBEDDING_MODEL = "togethercomputer/m2-bert-80M-2k-retrieval"
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+CHROMA_PATH = os.path.join(BASE_DIR, "chroma_storage")
+
+
+api_key = os.getenv("TOGETHER_API")
+ai_client = OpenAI(api_key=api_key, base_url="https://api.together.xyz/v1")
+
+
+def extract_pdf(pdf_path: str) -> str:
+
+    text = ""
+    with open(pdf_path, "rb") as file:
+        reader = pypdf.PdfReader(file)
+        for page_num in range(len(reader.pages)):
+            page = reader.pages[page_num]
+            text += page.extract_text()
+            text += "\n--PAGE BREAK--\n"
+
+    return text
+
+
+def create_vectorDB():
+    docs_paths = os.listdir(os.getcwd() + "/data_ingetion/firms_report/")
+
+    complete_text = ""
+
+    for doc_path in docs_paths:
+        complete_text += extract_pdf(
+            os.getcwd() + "/data_ingetion/firms_report/" + doc_path
+        )
+        complete_text += "\n\n"
+
+    splitter = RecursiveCharacterTextSplitter(
+        chunk_size=512,
+        chunk_overlap=84,
+        length_function=len,
+        is_separator_regex=False,
+    )
+
+    processed_docs = splitter.split_text(complete_text)
+    db_client = chromadb.PersistentClient(path=CHROMA_PATH)
+    collection = db_client.create_collection(VECTOR_NAME)
+
+    response = ai_client.embeddings.create(input=processed_docs, model=EMBEDDING_MODEL)
+    embeddings = [item.embedding for item in response.data]
+    unique_ids = [str(uuid.uuid4()) for _ in range(len(embeddings))]
+    collection.add(documents=processed_docs, embeddings=embeddings, ids=unique_ids)
+
+    return collection.name
+
+
+def get_relevant_chunks(query: str):
+
+    db_client = chromadb.PersistentClient(path=CHROMA_PATH)
+    found = VECTOR_NAME in [c.name for c in db_client.list_collections()]
+
+    if found:
+        collection = db_client.get_collection(VECTOR_NAME)
+    else:
+        collection = db_client.get_collection(create_vectorDB())
+
+    response = ai_client.embeddings.create(input=query, model=EMBEDDING_MODEL)
+    QE = response.data[0].embedding
+
+    relevant_chunks = collection.query(query_embeddings=QE, n_results=4)
+
+    processed = ""
+
+    for idx, doc in enumerate(relevant_chunks["documents"][0], start=1):
+        processed += f"Chunks number {idx}\n\n"
+        processed += doc + "\n\n"
+
+    return processed

main_api.py

@@ -0,0 +1,8 @@
+from fastapi import FastAPI
+from data_ingetion.data_api import app as data_app
+from orchestrator.orchestrator_api import app as or_app
+
+app = FastAPI()
+
+app.include_router(data_app, prefix="/data")
+app.include_router(or_app, prefix="/orchestrator")

orchestrator/orchestrator.py

@@ -0,0 +1,77 @@
+from openai import OpenAI
+from .prompts import ORCHESTRATOR_SYS_PROMPT, FINAL_SYS_PROMPT
+import requests
+import json
+import os
+
+api_key = os.getenv("TOGETHER_API")
+client = OpenAI(api_key=api_key, base_url="https://api.together.xyz/v1")
+
+
+def get_orchertration_resposne(query, history):
+
+    history = history[::-1][:5]
+    response = client.chat.completions.create(
+        model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+        messages=[
+            {
+                "role": "system",
+                "content": ORCHESTRATOR_SYS_PROMPT,
+            },
+            *history,
+            {"role": "user", "content": "Query: " + query},
+        ],
+    )
+    r = response.choices[0].message.content
+    data = json.loads(str(r))
+
+    if data["tool"] == "get_change":
+        result = requests.post(
+            url="http://127.0.0.1:8000/data/get_historical_data",
+            json=data["parameters"],
+        ).json()
+
+    elif data["tool"] == "get_earning":
+        result = requests.post(
+            url="http://127.0.0.1:8000/data/get_earning_metrics",
+            json=data["parameters"],
+        ).json()
+
+    elif data["tool"] == "get_portfolio_status":
+        result = requests.post(
+            url="http://127.0.0.1:8000/data/get_portfolio_data", json=data["parameters"]
+        ).json()
+
+    elif data["tool"] == "get_knowledge":
+        result = requests.post(
+            url="http://127.0.0.1:8000/data/get_knowledge", json=data["parameters"]
+        ).json()
+
+    elif data["tool"] == None:
+        return data["parameters"]
+
+    else:
+        result = {
+            "response": "An error occured internally please communicate this to user firmly"
+        }
+    return result
+
+
+def final_response(query, context, history):
+
+    history = history[::-1][:5]
+    response = client.chat.completions.create(
+        model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+        messages=[
+            {
+                "role": "system",
+                "content": FINAL_SYS_PROMPT,
+            },
+            *history,
+            {"role": "user", "content": f"Query : {query} \n\n Context: {context}"},
+        ],
+        stream=True,
+    )
+
+    for chunk in response:
+        yield chunk.choices[0].delta.content or ""

orchestrator/orchestrator_api.py

@@ -0,0 +1,30 @@
+from fastapi import APIRouter
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+
+from .orchestrator import get_orchertration_resposne, final_response
+
+app = APIRouter()
+
+
+class ODReq(BaseModel):
+    query: str
+    history: list = []
+
+
+class FinalReq(BaseModel):
+    query: str
+    history: list = []
+    context: str = ""
+
+
+@app.post("/orchestrator_decision")
+def get_OD(req: ODReq):
+    return get_orchertration_resposne(req.query, req.history)
+
+
+@app.post("/final_response")
+def get_final(req: FinalReq):
+    return StreamingResponse(
+        final_response(req.query, req.context, req.history), media_type="text/plain"
+    )

orchestrator/prompts.py

@@ -0,0 +1,55 @@
+ORCHESTRATOR_SYS_PROMPT = """
+You are in role of orchestrator and your task is to orchestrate a LLM based AI agent workflow working with tools to achive the goal of being a helpful Finance Assistant.
+
+You are provided with multiple tools and your task is to call these tool based on user request and use the returned response from tools to answer the user query.
+
+Tools available: 
+
+    To get information on stocks:
+        
+        get_change() -> use this tool to get infomration on change in stock price in given timeframe ie. Price[today]-price[today-period] percentage change.
+            parameters -> symbol: str the symbol of that ticker/stock YFinance style (use .NS for indian stocks), period: int number of days to get change for.
+
+        get_earning() -> use this tool to get infomration on year on year earning reports on the stock. 
+            parameters -> symbol: str the symbol of the ticker/stock YFinance style (use .NS for indian stocks) for which we need earning metrics.
+
+        get_portfolio_status() -> use this tool to get information on current portfolio structure and price changes to make informed decisions.
+            parameters -> region:str["IND","US"] Region for which we are fetching portfolio result. Currently only IND (india) and US(USA) supported
+
+        get_knowledge -> use this tool to get prior knowledge as context about the firm, its a RAG based tool ie. you will give the augmented user query for better retrival results.
+            parameters -> query:str User query augmented/expanded by you for better retrival results
+
+
+
+You have to respond in structured json format, mentioning tool name and prameter json.
+If the query is general and can be answered without any tool put "tool"=null (JSON null) and parameters just have one key value pair of "response":"your_response" to the query.
+
+For example:
+
+Query : What is the change in apple stock in past 1 month.
+
+response : 
+    {
+    "tool":"get_change",
+    "parameters" : {
+        "symbol" : "AAPL",
+        "period" : 31
+        }
+    }
+
+Query : Hi, how are you ?
+
+response : 
+    {
+    "tool":null,
+    "parameters" : {
+        "response" : "Hey! I am fine. How can i help you today?"
+        }
+    }
+
+
+Dont add any comments around json, you should only respond in valid json format only.
+"""
+FINAL_SYS_PROMPT = """
+You task to to generate final response of a long workflow/reseach. You will be provided with Query that is the original query and some context that is derived from different tools your task is to create a condensed output to effectively answer the user query. Try to be consise and to the point. Also add some disclamers if there is any uncertanity.
+"""

requirements.txt

@@ -0,0 +1,156 @@
+aenum==3.1.16
+aiofiles==24.1.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.12.2
+aiosignal==1.3.2
+altair==5.5.0
+annotated-types==0.7.0
+anyio==4.9.0
+asgiref==3.8.1
+async-timeout==5.0.1
+asyncio==3.4.3
+attrs==25.3.0
+backoff==2.2.1
+bcrypt==4.3.0
+beautifulsoup4==4.13.4
+blinker==1.9.0
+build==1.2.2.post1
+cachetools==5.5.2
+certifi==2025.4.26
+cffi==1.17.1
+charset-normalizer==3.4.2
+chromadb==1.0.11
+click==8.2.1
+coloredlogs==15.0.1
+curl_cffi==0.11.1
+dataclasses-json==0.6.7
+deepgram-sdk==4.0.0
+Deprecated==1.2.18
+deprecation==2.1.0
+distro==1.9.0
+dnspython==2.7.0
+durationpy==0.10
+email_validator==2.2.0
+exceptiongroup==1.3.0
+fastapi==0.115.9
+fastapi-cli==0.0.7
+filelock==3.18.0
+flatbuffers==25.2.10
+frozendict==2.4.6
+frozenlist==1.6.0
+fsspec==2025.5.1
+gitdb==4.0.12
+GitPython==3.1.44
+google-auth==2.40.2
+googleapis-common-protos==1.70.0
+grpcio==1.71.0
+h11==0.16.0
+hf-xet==1.1.2
+httpcore==1.0.9
+httptools==0.6.4
+httpx==0.28.1
+huggingface-hub==0.32.2
+humanfriendly==10.0
+idna==3.10
+importlib_metadata==8.6.1
+importlib_resources==6.5.2
+Jinja2==3.1.6
+jiter==0.10.0
+jsonpatch==1.33
+jsonpointer==3.0.0
+jsonschema==4.24.0
+jsonschema-specifications==2025.4.1
+kubernetes==32.0.1
+langchain-core==0.3.62
+langchain-text-splitters==0.3.8
+langsmith==0.3.42
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+marshmallow==3.26.1
+mdurl==0.1.2
+mmh3==5.1.0
+mpmath==1.3.0
+multidict==6.4.4
+multitasking==0.0.11
+mypy_extensions==1.1.0
+narwhals==1.41.0
+numpy==2.2.6
+oauthlib==3.2.2
+onnxruntime==1.22.0
+openai==1.82.0
+opentelemetry-api==1.33.1
+opentelemetry-exporter-otlp-proto-common==1.33.1
+opentelemetry-exporter-otlp-proto-grpc==1.33.1
+opentelemetry-instrumentation==0.54b1
+opentelemetry-instrumentation-asgi==0.54b1
+opentelemetry-instrumentation-fastapi==0.54b1
+opentelemetry-proto==1.33.1
+opentelemetry-sdk==1.33.1
+opentelemetry-semantic-conventions==0.54b1
+opentelemetry-util-http==0.54b1
+orjson==3.10.18
+overrides==7.7.0
+packaging==24.2
+pandas==2.2.3
+peewee==3.18.1
+pillow==11.2.1
+platformdirs==4.3.8
+posthog==4.2.0
+propcache==0.3.1
+protobuf==5.29.4
+pyarrow==20.0.0
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pycparser==2.22
+pydantic==2.11.5
+pydantic_core==2.33.2
+pydeck==0.9.1
+Pygments==2.19.1
+pypdf==5.5.0
+PyPika==0.48.9
+pyproject_hooks==1.2.0
+python-dateutil==2.9.0.post0
+python-dotenv==1.1.0
+python-multipart==0.0.20
+pytz==2025.2
+PyYAML==6.0.2
+referencing==0.36.2
+requests==2.32.3
+requests-oauthlib==2.0.0
+requests-toolbelt==1.0.0
+rich==14.0.0
+rich-toolkit==0.14.6
+rpds-py==0.25.1
+rsa==4.9.1
+shellingham==1.5.4
+six==1.17.0
+smmap==5.0.2
+sniffio==1.3.1
+soupsieve==2.7
+starlette==0.45.3
+streamlit==1.45.1
+sympy==1.14.0
+tenacity==9.1.2
+tokenizers==0.21.1
+toml==0.10.2
+tomli==2.2.1
+tornado==6.5.1
+tqdm==4.67.1
+typer==0.16.0
+typing-inspect==0.9.0
+typing-inspection==0.4.1
+typing_extensions==4.13.2
+tzdata==2025.2
+urllib3==2.4.0
+uvicorn==0.34.2
+uvloop==0.21.0
+verboselogs==1.7
+watchdog==6.0.0
+watchfiles==1.0.5
+websocket-client==1.8.0
+websockets==15.0.1
+wrapt==1.17.2
+yarl==1.20.0
+yfinance==0.2.61
+zipp==3.22.0
+zstandard==0.23.0

streamlit_app/app.py

@@ -0,0 +1,136 @@
+import streamlit as st
+import requests
+from deepgram import (
+    DeepgramClient,
+    DeepgramClientOptions,
+    PrerecordedOptions,
+    FileSource,
+)
+from io import BytesIO
+import os
+
+# Page configuration
+st.set_page_config(page_title="Market Brief Chat", page_icon="💬", layout="wide")
+DG_API = os.getenv("DG_API")
+
+# Initialize session state for chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = [
+        {"role": "assistant", "content": "How can I help you today?"}
+    ]
+if "um" not in st.session_state:
+    st.session_state.um = None
+
+# Initialize other session state variables
+if "is_recording" not in st.session_state:
+    st.session_state.is_recording = False
+
+
+def STT(buffer):
+
+    config: DeepgramClientOptions = DeepgramClientOptions(api_key=DG_API)
+    deepgram: DeepgramClient = DeepgramClient("", config)
+    payload: FileSource = {
+        "buffer": buffer,
+    }
+    # STEP 2: Configure Deepgram options for audio analysis
+    options = PrerecordedOptions(
+        model="nova-3",
+        smart_format=True,
+    )
+    # STEP 3: Call the transcribe_file method with the text payload and options
+    response = deepgram.listen.rest.v("1").transcribe_file(payload, options)
+
+    data = response.to_json()
+    transcript = data["results"]["channels"][0]["alternatives"][0]["transcript"]
+
+    return transcript
+
+
+def TTS(text):
+    DEEPGRAM_URL = "https://api.deepgram.com/v1/speak?model=aura-2-thalia-en"
+    DEEPGRAM_API_KEY = DG_API
+
+    payload = {"text": text}
+
+    headers = {
+        "Authorization": f"Token {DEEPGRAM_API_KEY}",
+        "Content-Type": "application/json",
+    }
+
+    # Create a BytesIO buffer to store audio
+    audio_buffer = BytesIO()
+
+    response = requests.post(DEEPGRAM_URL, headers=headers, json=payload)
+
+    audio_buffer.write(response.content)
+
+    # Move cursor to the beginning of the buffer
+    audio_buffer.seek(0)
+    return audio_buffer
+
+
+# App title
+st.markdown("<h1>💬 Market Brief Chat</h1>", unsafe_allow_html=True)
+st.markdown("---")
+
+
+# Display chat history
+chat_col, audio_col = st.columns([0.85, 0.15])
+with chat_col:
+    c = st.container(height=400, border=True)
+    with c:
+        for message in st.session_state.messages:
+            with st.chat_message(message["role"]):
+                st.write(message["content"])
+
+with audio_col:
+    # Voice input buttonif
+    data = st.audio_input(label="🎤 Record")
+    if data:
+        st.session_state.um = STT(data)
+    if st.button("🔊 listen"):
+        text = st.session_state.messages[-1]["content"]
+        buffer = TTS(text)
+        st.audio(data=buffer)
+        st.success("Playing")
+
+# Voice input button (beside text input conceptually)
+# Handle text input
+user_input = st.chat_input("Ask me about market trends...")
+st.session_state.um = user_input
+if st.session_state.um:
+    with c:
+        with st.chat_message("user"):
+            st.markdown(user_input)
+        st.session_state.messages.append(
+            {"role": "user", "content": st.session_state.um}
+        )
+
+        data = {"query": user_input, "history": []}
+        or_response = requests.post(
+            url="http://127.0.0.1:8000/orchestrator/orchestrator_decision", json=data
+        ).json()
+
+        print(or_response)
+
+        agent_response = ""
+        data = {"query": user_input, "context": or_response["response"]}
+        full_response = requests.post(
+            url="http://127.0.0.1:8000/orchestrator/final_response",
+            json=data,
+            stream=True,
+        )
+        with st.chat_message("assistant"):
+            placeholder = st.empty()
+            for chunk in full_response.iter_content(
+                decode_unicode=True, chunk_size=None
+            ):
+                agent_response += chunk
+                placeholder.markdown(agent_response + "▌")
+
+        st.session_state.messages.append(
+            {"role": "assistant", "content": agent_response}
+        )
+        st.session_state.um = None
+        st.rerun()