app.py

app.py

@@ -1,52 +1,43 @@
+import gradio as gr
 import requests
 from transformers import AutoTokenizer, AutoModelForCausalLM
-import gradio as gr
+import torch
 
-# 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")
+# Load the GPT-Neo 1.3B model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
+model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
 
-# 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'}
+# Function to fetch Bitcoin price from CoinGecko API
+def fetch_btc_price():
+    url = "https://api.coingecko.com/api/v3/simple/price"
+    params = {'ids': 'bitcoin', '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()
+        data = response.json()
+        return data['bitcoin']['usd']
+    return None
+
+# Generate crypto insights based on BTC price using GPT-Neo
+def generate_crypto_insight():
+    btc_price = fetch_btc_price()
+    if btc_price:
+        prompt = f"Bitcoin's current price is ${btc_price}. What is the future outlook for the cryptocurrency market? Provide advice for long-term investors."
+        inputs = tokenizer(prompt, return_tensors="pt")
+        outputs = model.generate(**inputs, max_length=150)
+        return tokenizer.decode(outputs[0], skip_special_tokens=True)
+    else:
+        return "Error fetching Bitcoin price."
+
+# Define the Gradio interface
+def crypto_analysis():
+    return generate_crypto_insight()
+
+# Create Gradio interface
+gr_interface = gr.Interface(fn=crypto_analysis, 
+                            inputs=None, 
+                            outputs="text", 
+                            title="Real-Time Crypto Analysis",
+                            description="Fetch the current Bitcoin price and get market insights using GPT-Neo.")
+
+# Launch the app
+gr_interface.launch()