Update app.py

- fix api issues

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import requests
-from transformers import pipeline
 
 # Fetch the API data once at startup
 API_URL = "https://dt074px2e9qbh.cloudfront.net/exploit-api.json"
@@ -11,21 +10,33 @@ except Exception as e:
     flagged_addresses = []
     print(f"Failed to load API data: {e}")
 
-# Load LLM for blockchain inference
-llm_pipeline = pipeline("text-generation", model="gpt-3.5-turbo")  # You can use a larger or more appropriate model
-
-# Function to infer chain using LLM
-def infer_chain(address):
+# Function to infer the blockchain using Hugging Face API
+def infer_chain(address, hf_token):
+    headers = {
+        "Authorization": f"Bearer {hf_token}"
+    }
     prompt = f"Identify the blockchain of the following cryptocurrency address: {address}"
-    result = llm_pipeline(prompt, max_length=50)
-    return result[0]["generated_text"].strip()
+
+    # Make the request to Hugging Face's Inference API
+    try:
+        response = requests.post(
+            "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1",
+            headers=headers,
+            json={"inputs": prompt}
+        )
+        response.raise_for_status()
+        result = response.json()
+        # Return the inferred blockchain (if available)
+        return result[0]["generated_text"].strip()
+    except Exception as e:
+        return "Unknown"  # Fallback if there's an error
 
 # Function to check if the address is flagged
-def check_flagged_address(address: str):
+def check_flagged_address(address: str, hf_token: str):
     """Check if a cryptocurrency address is flagged in the Bybit hack database."""
     
     # Infer the chain of the address
-    chain = infer_chain(address)
+    chain = infer_chain(address, hf_token)
     
     # Check if the address is in the API data
     flagged = any(entry.get("address", "").lower() == address.lower() for entry in flagged_addresses)
@@ -48,7 +59,7 @@ def chat_with_agent(user_input, chat_history, hf_token):
         return chat_history, ""
     
     # Run the address checker
-    response = check_flagged_address(user_input)
+    response = check_flagged_address(user_input, hf_token)
     
     # Append as OpenAI-style messages
     chat_history.append({"role": "user", "content": user_input})