app.py

from transformers import AutoTokenizer, AutoModelForCausalLM
import requests
import gradio as gr

# Load the model and tokenizer from Hugging Face
tokenizer = AutoTokenizer.from_pretrained("LaierTwoLabsInc/Satoshi-7B")
model = AutoModelForCausalLM.from_pretrained("LaierTwoLabsInc/Satoshi-7B")

# Function to fetch BTC 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:
        data = response.json()
        return data['bitcoin']['usd']
    return None

# Function to generate a response based on the prompt
def generate_custom_response(prompt):
    # Encode the input prompt
    inputs = tokenizer(prompt, return_tensors="pt")
    
    # Generate a response from the model
    outputs = model.generate(inputs['input_ids'], max_length=200, num_return_sequences=1)
    
    # Decode the generated response
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

# Function to fetch BTC price and generate analysis based on the prompt
def btc_analysis(prompt):
    btc_price = fetch_btc_price()
    if btc_price:
        full_prompt = f"Bitcoin's current price is ${btc_price}. {prompt}"
        ai_response = generate_custom_response(full_prompt)
        return ai_response
    else:
        return "Error fetching Bitcoin price."

# Gradio Interface for BTC analysis
interface = gr.Interface(
    fn=btc_analysis, 
    inputs=gr.Textbox(value="What does this price mean for investors and the market?", label="Prompt"),
    outputs="text",
    title="Bitcoin Price Analysis",
    description="Fetch Bitcoin's current price and get analysis based on the provided prompt using Hugging Face's model."
)

# Launch the Gradio app
interface.launch()