app.py

app.py

@@ -1,43 +1,24 @@
 import gradio as gr
-import requests
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# 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")
+# Load the smaller model and tokenizer
+model_name = "distilgpt2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# 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:
-        data = response.json()
-        return data['bitcoin']['usd']
-    return None
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=150, num_return_sequences=1)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
 
-# 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."
+# Set up Gradio interface
+iface = gr.Interface(
+    fn=generate_response, 
+    inputs="text", 
+    outputs="text", 
+    title="Crypto Analysis Model", 
+    description="Enter your prompt related to Bitcoin or cryptocurrency."
+)
 
-# 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()
+iface.launch()