Update app.py

app.py

@@ -15,46 +15,27 @@ import random
 import requests
 import os
 
-HUGGINGFACE_API_URL = "https://api-inference.huggingface.co/models/gpt2"
+from transformers import pipeline, set_seed
 
-
-
-HUGGINGFACE_API_KEY = os.environ.get("HUGGINGFACE_API_KEY" , "") 
+# Load a text generation model locally
+generator = pipeline('text-generation', model='gpt2')
+set_seed(42)
 
 # File to store feedback
 FEEDBACK_FILE = "user_feedback.csv"
 
 def huggingface_chatbot(user_input):
-    if not HUGGINGFACE_API_KEY:
-        return "Error: Hugging Face API key not configured."
     try:
-        headers = {
-            "Authorization": f"Bearer {HUGGINGFACE_API_KEY}",  # note the space after 'Bearer'
-            "Content-Type": "application/json"
-        }
-        data = {
-            "inputs": user_input,
-            "parameters": {
-                "max_new_tokens": 150,
-                "temperature": 0.7,
-                "return_full_text": False
-            }
-        }
-        response = requests.post(HUGGINGFACE_API_URL, headers=headers, json=data)
-        response.raise_for_status()
-        result = response.json()
+        result = generator(user_input, max_length=150, temperature=0.7, do_sample=True)
         if isinstance(result, list) and "generated_text" in result[0]:
             return result[0]["generated_text"]
-        elif "generated_text" in result:
-            return result["generated_text"]
-        elif "text" in result[0]:
-            return result[0]["text"]
         else:
-            return "⚠️ Could not parse response from LLaMA model."
+            return "⚠️ Could not parse model response."
     except Exception as e:
         return f"Error: {str(e)}"
 
 
+
 # Database setup for user authentication
 def init_db():
     conn = sqlite3.connect("users.db")