Update app.py

app.py

@@ -1,10 +1,11 @@
 import gradio as gr
-from huggingface_hub import InferenceClient, login  # Added login
+import os
+from huggingface_hub import InferenceClient, login
 from transformers import pipeline
 import random
 
-# Authenticate with Hugging Face (get token: https://huggingface.co/settings/tokens)
-login(token="YOUR_HF_TOKEN")  # 👈 Replace with your token!
+# Authenticate using secret environment variable 🔒
+login(token=os.environ.get("HF_TOKEN"))
 
 # Safety tools 🛡️
 BLOCKED_WORDS = ["violence", "hate", "gun", "personal"]
@@ -14,7 +15,7 @@ SAFE_IDEAS = [
     "Plan an AI tool for school safety 🚸"
 ]
 
-# Changed to PUBLIC safety model ✅
+# Safety model
 safety_checker = pipeline("text-classification", model="unitary/toxic-bert")
 
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
@@ -24,7 +25,7 @@ def is_safe(text):
     if any(bad_word in text for bad_word in BLOCKED_WORDS):
         return False
     result = safety_checker(text)[0]
-    return not (result["label"] == "toxic" and result["score"] > 0.7)  # Changed label check
+    return not (result["label"] == "toxic" and result["score"] > 0.7)
 
 def respond(message, history, system_message, max_tokens, temperature, top_p):
     if not is_safe(message):
@@ -35,7 +36,42 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
         "content": f"{system_message}\nYou are a friendly STEM mentor for kids. Never discuss unsafe topics!"
     }]
     
-    # Rest of your chat code...
-    # (Keep your existing chat implementation here)
+    # Rest of chat implementation
+    for user_msg, bot_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if bot_msg:
+            messages.append({"role": "assistant", "content": bot_msg})
     
-# Rest of your Gradio setup...
+    messages.append({"role": "user", "content": message})
+    
+    response = ""
+    for chunk in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p
+    ):
+        token = chunk.choices[0].delta.content
+        response += token
+        yield response
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 REACT Ethical AI Lab")
+    gr.ChatInterface(
+        respond,
+        additional_inputs=[
+            gr.Textbox("You help students create ethical AI projects.", label="Guidelines"),
+            gr.Slider(128, 1024, value=512, label="Max Response Length"),
+            gr.Slider(0.1, 1.0, value=0.3, label="Creativity Level"),
+            gr.Slider(0.7, 1.0, value=0.85, label="Focus Level")
+        ],
+        examples=[
+            ["How to build a robot that plants trees?"],
+            ["Python code for a pollution sensor"]
+        ]
+    )
+
+if __name__ == "__main__":
+    demo.launch()