app.py

app.py

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+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()