Update app.py

app.py

@@ -1,47 +1,88 @@
 import gradio as gr
+import requests
 from openai import OpenAI
 import os
 
+# Load your API keys from environment variables
 ACCESS_TOKEN = os.getenv("HF_TOKEN")
-print("Access token loaded.")
 TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
+print("Access token loaded.")
 client = OpenAI(
     base_url="https://api-inference.huggingface.co/v1/",
     api_key=ACCESS_TOKEN,
 )
 print("OpenAI client initialized.")
 
-# Search Tool
-from langchain_community.tools.tavily_search import TavilySearchResults
-search_tool = TavilySearchResults(tavily_api_key=TAVILY_API_KEY)
 # Define a comprehensive system prompt
 SYSTEM_PROMPT = """
 You are a highly knowledgeable and reliable Crypto Trading Advisor and Analyzer. 
 Your goal is to assist users in understanding, analyzing, and making informed decisions about cryptocurrency trading. 
-You provide accurate, concise, and actionable advice based on real-time data, historical trends, and established best practices.
 """
 
+# Binance API - Fetch Market Data
+def get_binance_data(symbol: str):
+    # Base URL for Binance API
+    url = f'https://api.binance.com/api/v3/ticker/24hr?symbol={symbol.upper()}'
+
+    try:
+        # Send GET request to Binance API
+        response = requests.get(url)
+        data = response.json()
+
+        if response.status_code != 200:
+            return {"error": "Error fetching data from Binance"}
+
+        # Extract relevant information from the API response
+        price = float(data['lastPrice'])
+        volume = float(data['volume'])
+        market_cap = float(data['quoteVolume'])  # Binance doesn't provide market cap directly, so we use quote volume as a proxy
+        change_24h = float(data['priceChangePercent'])
+
+        return {
+            'price': price,
+            'volume': volume,
+            'market_cap': market_cap,
+            'change_24h': change_24h
+        }
+    except Exception as e:
+        return {"error": f"An error occurred: {str(e)}"}
+
 # Function to handle chatbot responses
 def respond(
     message,
-    history: list[tuple[str, str]],
-    max_tokens,
-    temperature,
-    top_p,
-    frequency_penalty,
-    seed
+    history: list[tuple[str, str]]
 ):
     print(f"Received message: {message}")
     print(f"History: {history}")
 
-    # Convert seed to None if -1 (meaning random)
-    if seed == -1:
-        seed = None
+    # Default values for the parameters
+    max_tokens = 1024
+    temperature = 0.3
+    top_p = 0.95
+    frequency_penalty = 0.0
+    seed = None
 
-    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
-    print("System prompt added to messages.")
+    if "crypto" in message.lower():
+        # Extract the cryptocurrency symbol from the message
+        crypto_symbol = message.split()[0].upper() + "USDT"  # Example: "Bitcoin" -> "BTCUSDT"
+        market_data = get_binance_data(crypto_symbol)
+
+        if 'error' in market_data:
+            response = "Error fetching data for this cryptocurrency."
+            yield response
+            return
 
-    # Add conversation history to the context
+        # Include real-time data in the system prompt
+        SYSTEM_PROMPT += f"""
+        Current Data for {crypto_symbol}:
+        - Price: ${market_data['price']}
+        - 24h Change: {market_data['change_24h']}%
+        - Volume: {market_data['volume']}
+        - Market Cap (proxy via quote volume): ${market_data['market_cap']}
+        """
+
+    # Prepare messages for the assistant
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
     for val in history:
         user_part = val[0]
         assistant_part = val[1]
@@ -50,13 +91,11 @@ def respond(
         if assistant_part:
             messages.append({"role": "assistant", "content": assistant_part})
 
-    # Append the latest user message
+    # Add the latest user message
     messages.append({"role": "user", "content": message})
 
     # Start response generation
     response = ""
-    print("Sending request to OpenAI API.")
-
     for message_chunk in client.chat.completions.create(
         model="meta-llama/Llama-3.3-70B-Instruct",
         max_tokens=max_tokens,
@@ -76,54 +115,11 @@ def respond(
 # Gradio UI
 chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Ask about crypto trading or analysis.", likeable=True)
 
-max_tokens_slider = gr.Slider(
-    minimum=1,
-    maximum=4096,
-    value=512,
-    step=1,
-    label="Max new tokens"
-)
-temperature_slider = gr.Slider(
-    minimum=0.1,
-    maximum=4.0,
-    value=0.7,
-    step=0.1,
-    label="Temperature"
-)
-top_p_slider = gr.Slider(
-    minimum=0.1,
-    maximum=1.0,
-    value=0.95,
-    step=0.05,
-    label="Top-P"
-)
-frequency_penalty_slider = gr.Slider(
-    minimum=-2.0,
-    maximum=2.0,
-    value=0.0,
-    step=0.1,
-    label="Frequency Penalty"
-)
-seed_slider = gr.Slider(
-    minimum=-1,
-    maximum=65535,
-    value=-1,
-    step=1,
-    label="Seed (-1 for random)"
-)
-
 demo = gr.ChatInterface(
     fn=respond,
-    additional_inputs=[
-        max_tokens_slider,
-        temperature_slider,
-        top_p_slider,
-        frequency_penalty_slider,
-        seed_slider,
-    ],
     fill_height=True,
     chatbot=chatbot,
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()