Upload folder using huggingface_hub

Dockerfile

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+# Use official Python slim image
+FROM python:3.11-slim
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1 \
+    DEBIAN_FRONTEND=noninteractive
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    git \
+    build-essential \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements file
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+# Copy the rest of the app
+COPY . .
+
+# Expose port for Streamlit
+EXPOSE 7860
+
+# Command to run the Streamlit app
+CMD ["streamlit", "run", "ui/app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.headless=true"]

README.md

@@ -1,10 +1,18 @@
----
-title: Chatbot App
-emoji: 🦀
-colorFrom: gray
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Market Research AI Chatbot
+
+This is a Streamlit-based AI chatbot for market research. It supports:
+
+- Predefined prompts in the sidebar (with truncated display and tooltip for full text)
+- Chat interface with latest messages at the top
+- Enter key submission for messages
+- AI responses using `MarketResearchAgent`
+
+## 🚀 How to Run Locally
+
+1. Clone the repo:
+
+```bash
+git clone <repo_url>
+cd <repo_folder>
+python run.py
+```

SETUP.md

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+### Setting up .venv
+```bash
+conda create --prefix /home/azureuser/ws/agenticai/projects/chatbot/.venv python=3.11 -y
+
+conda activate /home/azureuser/ws/agenticai/projects/chatbot/.venv
+
+conda deactivate 
+
+uv pip install -r requirements.txt
+```
+
+### Run Unit Tests
+```bash
+pytest -v tests/test_data_agent.py
+
+python -m pytest -v
+
+```

appagents/research_agent.py

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+from tools.google_tools import GoogleTools
+from tools.news_tools import NewsTools
+from tools.yahoo_tools import FinanceTools
+from agents import Agent
+
+class MarketResearchAgent:
+    """
+    Encapsulates the AI agent definition for market research.
+    """
+
+    @staticmethod
+    def create():
+        """
+        Returns a configured Agent instance ready for use.
+        """
+        tools = [
+            GoogleTools.search,
+            FinanceTools.get_summary,
+            FinanceTools.get_history,
+            NewsTools.top_headlines,
+            NewsTools.search_news,
+        ]
+
+        instructions = """
+        You are a research assistant. Your primary goal is to give accurate, concise, and well-sourced answers.
+
+        If the question involves current events, factual data, or recent updates, always call the provided tools (like Google or News) to ensure information is current.
+
+        Prefer factual precision over opinion. Summarize retrieved results into clear, user-friendly responses.
+        """
+
+        agent = Agent(
+            name="AI Assistant",
+            tools=tools,
+            instructions=instructions,
+            model="gpt-4o-mini"
+        )
+        return agent

core/logger.py

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+import functools
+import datetime
+
+def log_call(func):
+    """
+    A decorator that logs when a function is called and when it finishes.
+    """
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        arg_list = ", ".join(
+            [repr(a) for a in args] + [f"{k}={v!r}" for k, v in kwargs.items()]
+        )
+        print(f"[{timestamp}] 🚀 Calling: {func.__name__}({arg_list})")
+        try:
+            result = func(*args, **kwargs)
+            print(f"[{timestamp}] ✅ Finished: {func.__name__}")
+            return result
+        except Exception as e:
+            print(f"[{timestamp}] ❌ Error in {func.__name__}: {e}")
+            raise
+    return wrapper

prompts/company_analysis.txt

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+Analyze the recent performance of Apple Inc. (AAPL).
+Include stock price trends, recent news, and any important announcements.

prompts/crypto_update.txt

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+Give a summary of the current cryptocurrency market.
+Include top performers, losers, and overall market sentiment.

prompts/economic_news.txt

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+Provide an update on major economic indicators released today.
+Include interest rates, unemployment data, and other relevant statistics.

prompts/market_overview.txt

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+Provide a concise overview of the current stock market trends and sentiment today.
+Include any notable market-moving news or events.

prompts/market_sentiment.txt

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+What is the current market sentiment based on news and financial reports?
+Provide a brief analysis of investor confidence and market outlook.

prompts/tech_news.txt

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+Summarize the latest technology news from the past 24 hours.
+Highlight any major product launches, acquisitions, or trends.

requirements.txt

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+openai==1.85.0
+    # via
+    #   agents (pyproject.toml)
+    #   autogen-ext
+    #   langchain-openai
+    #   openai-agents
+    #   semantic-kernel
+openai-agents==0.0.17
+    # via agents (pyproject.toml)
+python-dotenv>=1.0.1
+requests>=2.31.0
+    # via
+    #   agents (pyproject.toml)
+    #   autogen-ext
+    #   langchain-openai
+    #   openai-agents
+    #   semantic-kernel
+yfinance>=0.2.27
+    # via tools/news_tools.py, tools/yahoo_tools.py
+streamlit

run.py

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+import os
+import subprocess
+
+# Run the Streamlit app with automatic reload on file changes
+subprocess.run([
+    "streamlit",
+    "run",
+    os.path.join("ui", "app.py"),
+    "--server.runOnSave", "true"
+])

tools/google_tools.py

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+import os
+import requests
+import yfinance as yf
+from dotenv import load_dotenv
+from agents import function_tool
+from core.logger import log_call
+
+# Load environment variables once
+load_dotenv()
+
+
+# ============================================================
+# 🔹 GOOGLE SEARCH TOOLSET (Serper.dev API)
+# ============================================================
+class GoogleTools:
+    """Provides tools for web search using Serper.dev (Google Search API)."""
+
+    @staticmethod
+    @function_tool
+    @log_call
+    def search(query: str, num_results: int = 3) -> str:
+        """
+        Perform a general Google search using the Serper.dev API.
+
+        Args:
+            query (str): The search query string.
+            num_results (int): Number of results to return.
+
+        Returns:
+            str: Formatted search results.
+        """
+        try:
+            api_key = os.getenv("SERPER_API_KEY")
+            if not api_key:
+                return "Missing SERPER_API_KEY in environment variables."
+
+            url = "https://google.serper.dev/search"
+            headers = {"X-API-KEY": api_key, "Content-Type": "application/json"}
+            payload = {"q": query, "num": num_results}
+
+            response = requests.post(url, headers=headers, json=payload)
+            response.raise_for_status()
+            data = response.json()
+
+            if "organic" not in data:
+                return "No results found."
+
+            formatted_results = [
+                f"Title: {item.get('title')}\n"
+                f"Link: {item.get('link')}\n"
+                f"Snippet: {item.get('snippet', '')}\n"
+                for item in data["organic"][:num_results]
+            ]
+            return "\n".join(formatted_results)
+
+        except Exception as e:
+            return f"Error performing Google search: {e}"
+
+
+# # ============================================================
+# # 🔹 OPENAI & OTHER MODEL APIs (optional future grouping)
+# # ============================================================
+# class ModelTools:
+#     """Provides access to LLM APIs like OpenAI, Gemini, or Groq."""
+
+#     @staticmethod
+#     @function_tool
+#     def query_openai(prompt: str, model: str = "gpt-4o-mini") -> str:
+#         """Query OpenAI model."""
+#         try:
+#             client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+#             response = client.chat.completions.create(
+#                 model=model,
+#                 messages=[{"role": "user", "content": prompt}],
+#             )
+#             return response.choices[0].message.content
+#         except Exception as e:
+#             return f"Error querying OpenAI API: {e}"

tools/news_tools.py

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+import os
+import requests
+import yfinance as yf
+from dotenv import load_dotenv
+from agents import function_tool
+from core.logger import log_call
+
+# Load environment variables once
+load_dotenv()
+
+
+# ============================================================
+# 🔹 NEWS TOOLSET (NewsAPI.org)
+# ============================================================
+class NewsTools:
+    """Provides tools to fetch top headlines and topic-based news."""
+
+    @staticmethod
+    @function_tool
+    @log_call
+    def top_headlines(country: str = "us", num_results: int = 5) -> str:
+        """Fetch top headlines for a given country."""
+        return NewsTools._fetch_news(query="", country=country, num_results=num_results)
+
+    @staticmethod
+    @function_tool
+    @log_call
+    def search_news(query: str, num_results: int = 5) -> str:
+        """Search for recent news articles about a specific topic."""
+        return NewsTools._fetch_news(query=query, country="", num_results=num_results)
+
+    @staticmethod
+    @log_call
+    def _fetch_news(query: str, country: str, num_results: int) -> str:
+        """Internal helper for NewsAPI.org."""
+        try:
+            api_key = os.getenv("NEWS_API_KEY")
+            if not api_key:
+                return "Missing NEWS_API_KEY in environment variables."
+
+            if query:
+                url = "https://newsapi.org/v2/everything"
+                params = {
+                    "q": query,
+                    "pageSize": num_results,
+                    "apiKey": api_key,
+                    "sortBy": "publishedAt",
+                    "language": "en"
+                }
+            else:
+                url = "https://newsapi.org/v2/top-headlines"
+                params = {
+                    "country": country or "us",
+                    "pageSize": num_results,
+                    "apiKey": api_key
+                }
+
+            response = requests.get(url, params=params)
+            response.raise_for_status()
+            data = response.json()
+
+            if not data.get("articles"):
+                return f"No news found for '{query or country}'."
+
+            formatted = [
+                f"📰 {a.get('title')}\n"
+                f"   Source: {a.get('source', {}).get('name')}\n"
+                f"   URL: {a.get('url')}\n"
+                for a in data["articles"][:num_results]
+            ]
+            return "\n".join(formatted)
+
+        except requests.exceptions.RequestException as e:
+            return f"Network error while calling News API: {e}"
+        except Exception as e:
+            return f"Unexpected error fetching news: {e}"

tools/yahoo_tools.py

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+import os
+import requests
+import yfinance as yf
+from dotenv import load_dotenv
+from agents import function_tool
+from core.logger import log_call
+
+# Load environment variables once
+load_dotenv()
+
+
+# ============================================================
+# 🔹 YAHOO FINANCE TOOLSET
+# ============================================================
+class FinanceTools:
+    """Provides tools for fetching stock, crypto, or ETF data from Yahoo Finance."""
+
+    @staticmethod
+    @function_tool
+    @log_call
+    def get_summary(symbol: str, period: str = "1d", interval: str = "1h") -> str:
+        """Fetch summary and price data for a ticker."""
+        try:
+            ticker = yf.Ticker(symbol)
+            data = ticker.history(period=period, interval=interval)
+
+            if data.empty:
+                return f"No data found for symbol '{symbol}'."
+
+            latest = data.iloc[-1]
+            current_price = round(latest["Close"], 2)
+            open_price = round(latest["Open"], 2)
+            change = round(current_price - open_price, 2)
+            pct_change = round((change / open_price) * 100, 2)
+
+            info = ticker.info
+            long_name = info.get("longName", symbol)
+            currency = info.get("currency", "USD")
+
+            formatted = [
+                f"📈 {long_name} ({symbol})",
+                f"Current Price: {current_price} {currency}",
+                f"Change: {change} ({pct_change}%)",
+                f"Open: {open_price} | High: {round(latest['High'], 2)} | Low: {round(latest['Low'], 2)}",
+                f"Volume: {int(latest['Volume'])}",
+                f"Period: {period} | Interval: {interval}",
+            ]
+            return "\n".join(formatted)
+
+        except Exception as e:
+            return f"Error fetching data for '{symbol}': {e}"
+
+    @staticmethod
+    @function_tool
+    @log_call
+    def get_history(symbol: str, period: str = "1mo") -> str:
+        """Fetch historical data for a given ticker."""
+        try:
+            ticker = yf.Ticker(symbol)
+            data = ticker.history(period=period)
+            if data.empty:
+                return f"No historical data found for '{symbol}'."
+            return f"Historical data for {symbol} ({period}):\n{data.tail(5).to_string()}"
+        except Exception as e:
+            return f"Error fetching historical data: {e}"

ui/app.py

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+import streamlit as st
+import os
+import glob
+import asyncio
+import sys
+
+# Add project root to sys.path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+from appagents.research_agent import MarketResearchAgent
+from agents import Runner, trace
+
+# -----------------------------
+# Load predefined prompts
+# -----------------------------
+def load_prompts(folder="prompts"):
+    prompts = []
+    for file_path in glob.glob(os.path.join(folder, "*.txt")):
+        with open(file_path, "r") as f:
+            content = f.read().strip()
+            if content:
+                prompts.append(content)
+    return prompts
+
+prompts = load_prompts()
+
+# -----------------------------
+# Streamlit page config
+# -----------------------------
+st.set_page_config(page_title="Market Research AI", layout="wide")
+
+# -----------------------------
+# Session state
+# -----------------------------
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+
+if "input_value" not in st.session_state:
+    st.session_state.input_value = ""
+
+# -----------------------------
+# Function to fetch AI response
+# -----------------------------
+async def get_ai_response(prompt):
+    agent = MarketResearchAgent.create()
+    with trace("Chatting with AI"):
+        result = await Runner.run(agent, prompt)
+        return result.final_output
+
+# -----------------------------
+# Sidebar prompts (2-line truncation with tooltip)
+# -----------------------------
+st.sidebar.title("Predefined Prompts")
+
+for idx, prompt_text in enumerate(prompts):
+    # Truncate to 2 lines (~80 characters per line)
+    truncated = prompt_text
+    if len(prompt_text) > 160:
+        truncated = prompt_text[:157] + "..."
+    # Use a container for each button
+    with st.sidebar.container():
+        # Show the truncated prompt as a button
+        if st.button(truncated, key=f"prompt_{idx}"):
+            # Add user message
+            st.session_state.chat_history.insert(0, {"role": "user", "message": prompt_text})
+            # Fetch AI response
+            response = asyncio.run(get_ai_response(prompt_text))
+            st.session_state.chat_history.insert(0, {"role": "assistant", "message": response})
+        # Show tooltip below (hover shows full prompt)
+        st.markdown(f"<span title='{prompt_text}' style='font-size:10px;color:gray;'>Hover to see full prompt</span>", unsafe_allow_html=True)
+
+
+# -----------------------------
+# Main chat area
+# -----------------------------
+st.title("Market Research AI Chat")
+
+# -----------------------------
+# Chat input with Enter key submit (using st.form)
+# -----------------------------
+with st.form(key="chat_form", clear_on_submit=True):
+    user_input = st.text_input(
+        "Type your message here:",
+        value=st.session_state.input_value,
+        placeholder="Write a message...",
+        key="chat_input"
+    )
+    send_button = st.form_submit_button("Send")
+
+if send_button and user_input.strip():
+    message = user_input.strip()
+    # Add user message
+    st.session_state.chat_history.insert(0, {"role": "user", "message": message})
+    # Get AI response
+    response = asyncio.run(get_ai_response(message))
+    st.session_state.chat_history.insert(0, {"role": "assistant", "message": response})
+    st.session_state.input_value = ""  # input will be cleared automatically by form
+
+# -----------------------------
+# Display chat history
+# -----------------------------
+if st.session_state.chat_history:
+    chat_style = """
+        <style>
+        .chat-container {
+            display: flex;
+            flex-direction: column; /* latest messages on top */
+            border: 1px solid #ccc;
+            padding: 10px;
+            border-radius: 8px;
+            background-color: #fafafa;
+        }
+        </style>
+    """
+    st.markdown(chat_style, unsafe_allow_html=True)
+
+    chat_html = '<div class="chat-container">'
+    for chat in st.session_state.chat_history:
+        if chat["role"] == "user":
+            chat_html += (
+                f"<div style='display:flex; align-items:center; justify-content:flex-end; margin:5px;'>"
+                f"<div style='background-color: #daf1fc; padding:10px; border-radius:10px; max-width:70%;'>{chat['message']}</div>"
+                f"<span style='font-size:28px; margin-left:8px;'>👤</span>"
+                f"</div>"
+            )
+        else:
+            chat_html += (
+                f"<div style='display:flex; align-items:center; justify-content:flex-start; margin:5px;'>"
+                f"<span style='font-size:28px; margin-right:8px;'>🤖</span>"
+                f"<div style='background-color: #f1f0f0; padding:10px; border-radius:10px; max-width:70%;'>{chat['message']}</div>"
+                f"</div>"
+            )
+    chat_html += '</div>'
+
+    st.markdown(chat_html, unsafe_allow_html=True)