Deploy updated Crypto Analyst Agent with Gradio UI

app.py

@@ -0,0 +1,242 @@
+# import libraries, APis and LLMs
+from crewai import Agent, Task, Crew
+import os
+from dotenv import load_dotenv
+from pathlib import Path
+from tools.market_data import MarketDataTool
+from tools.sentiment_tool import SentimentTool
+from tools.historical_data_tool import HistoricalDataTool
+from tools.analytics_tool import AnalyticsTool
+import json
+import gradio as gr
+import warnings
+
+warnings.filterwarnings("ignore")
+
+# Load environment variables (from local .env or Hugging Face secrets)
+load_dotenv()
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+SERPER_API_KEY = os.getenv("SERPER_API_KEY")
+
+# Optional: set CHROMA_OPENAI_API_KEY for embedding compatibility
+os.environ["CHROMA_OPENAI_API_KEY"] = OPENAI_API_KEY
+
+os.environ["OPENAI_MODEL_NAME"] = "gpt-5-mini"
+
+#Tools
+market_data_tool = MarketDataTool()
+sentiment_tool = SentimentTool()
+historical_data_tool= HistoricalDataTool()
+analytics_tool = AnalyticsTool()
+
+
+
+# Define the agents
+market_agent = Agent(
+    role="Crypto Market Analyst",
+    goal="Fetches live market prices using the CoinGecko API and summarize trends.",
+    backstory="A data-driven analyst who monitors cryptocurrency movements"
+        "and provides concise and accurate market insights",
+    verbose=False,
+    allow_delegations=True,
+    tools = [market_data_tool]
+)
+
+sentiment_agent = Agent(
+    role="Crypto Sentiment Analyst",
+    goal="Analyze public and media sentiment about cryptocurrencies to gauge market mood.",
+    backstory= "You are an expert at analyzing news headlines and social media chatter to assess whether sentiment is bullish or bearish.",
+    verbose = False,
+    allow_delegations=True,
+    tools=[sentiment_tool]
+)
+
+historical_agent = Agent(
+    role="Crypto Historical Analyst",
+    goal="Analyze past cryptocurrency trends using historical data.",
+    backstory=(
+        "An experienced quantitative analyst who studies long-term "
+        "price behavior, volatility, and trend strength using CoinGecko data."
+    ),
+    verbose=True,
+    allow_delegations=True,
+    tools=[historical_data_tool],
+)
+
+analytics_agent = Agent(
+    role="Cryptocurrency Analytics",
+    goal="Integrate metrics from the market agent, historical agent, and sentiment agent"
+        "and anlyze them to devise a coherent market view.",
+    backstory=(
+        "You are an expert quantitative strategist who synthesizes trend, sentiment and volatility data"
+        "to produce both structured metrics and qualitative market insights."
+    ),
+    verbose=True,
+    allow_delegations=False,
+    tools=[analytics_tool]
+)
+
+strategy_agent = Agent(
+    role="Crypto Strategy Analyst",
+    goal=(
+        "Transform analytical metrics and sentiment data into an actionable trading plan. "
+        "Determine directional bias, entry/exit strategies, and risk management guidance "
+        "based on risk, opportunity, and sentiment metrics."
+    ),
+    backstory=(
+        "A senior crypto strategist with a background in quantitative finance and "
+        "macro analysis. They integrate market structure, sentiment, and volatility "
+        "to form adaptive yet risk-aware trading stances."
+    ),
+    verbose=True,
+)
+
+reporting_agent = Agent(
+    role="Crypto Reporting Analyst",
+    goal=(
+        "Compile a concise, professional, and readable summary report "
+        "from all agents' outputs. The report should combine numerical analytics, "
+        "market context, sentiment, and trading strategy into one clear narrative."
+    ),
+    backstory=(
+        "You are a senior market strategist and financial writer who creates daily reports "
+        "for traders and institutional clients. You blend analytical data, sentiment, "
+        "and technical insight into clear, actionable narratives."
+    ),
+    verbose=True,
+)
+
+# Define the tasks
+
+market_data_task = Task(
+    description=("Retrieve the live market price for {cryptocurrency_selection} in {currency_selection} and summarize briefly"),
+    expected_output=("Summary of the {cryptocurrency_selection} price in {currency_selection}"),
+    agent=market_agent
+)
+
+sentiment_task = Task(
+    description="Fetch sentiment for {cryptocurrency_selection} from Google News and Reddit, "
+    "then using expert intuition classify it as bullish, bearish or neutral.",
+    expected_output="Short summary with a sentiment classification.",
+    async_execution=True,
+    agent=sentiment_agent
+)
+
+historical_data_task = Task(
+    description="Fetch and summarize {cryptocurrency_selection}'s {currency_selection} price trend over the past {days_selection} days.",
+    expected_output="A short summary including start price, end price, percent change, volatility, and trend direction.",
+    agent=historical_agent,
+)
+
+analytics_task = Task(
+    description=(
+        "Combine the results of the Market, Historical and Sentiment agents for {cryptocurrency_selection}."
+        "First, use the 'analytics_tool' tool to compute structured metrics."
+        "Then, interpret those metrics into an analytical narrative summarizing the overall market condition, risks and opportunites for that cryptocurrency"
+    ),
+    expected_output=(
+        "A JSON object containing structured indicators from the tool"
+        "and an LLM-generated interpretation explaining what they mean."
+    ),
+    agent=analytics_agent,
+    inputs={
+        "market_data": "{output_of_market_data_task}",
+        "historical_data": "{output_of_historical_data_task}",
+        "sentiment_data": "{output_of_sentiment_data_task}"
+    }
+)
+
+strategy_task = Task(
+    description=(
+        "Based on the structured analytics output for {cryptocurrency_selection}, "
+        "derive a clear trading stance. Evaluate the metrics such as sentiment_confidence, "
+        "risk_score_out_of_10, opportunity_score_out_of_10, volatility, and RSI. "
+        "Formulate an actionable trading bias (e.g., long, neutral, short) with reasoning. "
+        "Provide position size guidance, entry/exit levels, and a brief risk management plan."
+    ),
+    expected_output=(
+        "JSON object with fields: bias, position_size, entry_strategy, exit_strategy, "
+        "risk_management, and reasoning."
+    ),
+    agent=strategy_agent,
+)
+
+reporting_task = Task(
+    description=(
+        "Generate a cohesive summary report for {cryptocurrency_selection}. "
+        "Combine data and insights from the Market, Historical, Sentiment, Analytics, "
+        "and Strategy agents. The report should read like a professional crypto analysis "
+        "briefing with structured Markdown sections."
+    ),
+    expected_output=(
+        "A Markdown-formatted report with sections:\n"
+        "1️⃣ Market Overview\n"
+        "2️⃣ Historical Performance\n"
+        "3️⃣ Sentiment Analysis\n"
+        "4️⃣ Analytical Summary\n"
+        "5️⃣ Strategy Outlook\n"
+        "6️⃣ Final Takeaways\n"
+        "Each section should contain concise, data-backed explanations and actionable insights."
+    ),
+    agent=reporting_agent,
+)
+
+# Create a crew
+crypto_analysis_crew = Crew(
+    agents=[market_agent, historical_agent, sentiment_agent, analytics_agent, strategy_agent, reporting_agent],
+    tasks=[market_data_task, historical_data_task, sentiment_task, analytics_task, strategy_task, reporting_task],
+    process = "sequential",
+    verbose=True
+    )
+
+
+
+# define the main gradio handler
+def generate_report(crypto_name, currency, days):
+    crypto_trading_inputs={
+    "cryptocurrency_selection": crypto_name.lower(),
+    "currency_selection": currency.lower(),
+    "days_selection": int(days),
+    }
+
+    result = crypto_analysis_crew.kickoff(inputs=crypto_trading_inputs)
+    
+    # Ensure Markdown rendering
+    if isinstance(result, dict) and "final_output" in result:
+        return result["final_output"]
+    return str(result)
+
+#Gradio interface
+with gr.Blocks(theme=gr.themes.Monochrome()) as app:
+    gr.Markdown("# 🪙 Crypto Intelligence Dashboard")
+    gr.Markdown("Run a full multi-agent analysis with adjustable lookback period.")
+
+    with gr.Row():
+        crypto = gr.Textbox(
+            label="Enter Cryptocurrency Name (e.g bitcoin, ethereum, cardano)",
+            placeholder="Type any crytocurrency name...",
+            value="bitcoin"
+        )
+        currency = gr.Dropdown(
+            ["usd", "eur", "gbp"],
+            label="Select Currency",
+            value="usd"
+        )
+        days = gr.Slider(
+            30, 730, value=365, step=15,
+            label="Days to analyze (Historical Range)"
+        )
+
+    run_button = gr.Button("🚀 Run Full Analysis", variant="primary")
+    report_output = gr.Markdown(label="📊 Intelligence Report")
+
+    run_button.click(
+    generate_report,
+    inputs=[crypto, currency, days],
+    outputs=report_output,
+    show_progress=True  
+    )
+
+if __name__ == "__main__":
+    app.launch(server_name="0.0.0.0", server_port=7860, share=True)