app.py

app.py

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+import requests
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+# Load LLaMA-like model from Hugging Face (replace with actual model)
+tokenizer = AutoTokenizer.from_pretrained("LaierTwoLabsInc/Satoshi-7B")
+model = AutoModelForCausalLM.from_pretrained("LaierTwoLabsInc/Satoshi-7B")
+
+# Function to fetch current crypto price from CoinGecko
+def fetch_crypto_price(crypto_id):
+    url = f"https://api.coingecko.com/api/v3/simple/price"
+    params = {'ids': crypto_id, 'vs_currencies': 'usd'}
+    response = requests.get(url, params=params)
+    if response.status_code == 200:
+        return response.json().get(crypto_id, {}).get('usd', 'Unavailable')
+    return "Error fetching price"
+
+# Function to fetch crypto news (dummy placeholder function, replace with an actual news API)
+def fetch_crypto_news():
+    # You can replace this with a real API call (e.g., from NewsAPI or CoinTelegraph API)
+    return "Latest news: Bitcoin continues to rise amid market uncertainties."
+
+# Function to generate AI analysis from LLaMA model
+def generate_crypto_analysis(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=150)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Main function that integrates real-time data and model analysis
+def analyze_crypto():
+    # Fetch real-time data
+    btc_price = fetch_crypto_price("bitcoin")
+    crypto_news = fetch_crypto_news()
+
+    # Create prompt based on fetched data
+    prompt = f"Bitcoin's current price is ${btc_price}. {crypto_news}. What does this mean for investors?"
+
+    # Generate analysis using the LLaMA model
+    analysis = generate_crypto_analysis(prompt)
+
+    # Return final analysis
+    return f"Price: ${btc_price}\nNews: {crypto_news}\nAI Analysis: {analysis}"
+
+# Gradio Interface
+def crypto_dashboard():
+    return analyze_crypto()
+
+# Create Gradio interface for real-time crypto analysis
+iface = gr.Interface(fn=crypto_dashboard, inputs=[], outputs="text")
+
+# Launch Gradio app
+iface.launch()