Update app.py

app.py

@@ -1,64 +1,54 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import os
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Configuration
+DEFAULT_API_KEY = "your-space-key-123"  # Change this!
+SYSTEM_PROMPT = """You are a mood-based recipe assistant. Follow these steps:
+1. Detect the user's mood from their message
+2. Suggest 3 recipes matching their mood and preferences
+3. Format with emojis and clear instructions"""
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+def generate_response(message, history=None, api_key=None):
+    """Secure response generation with API key check"""
+    if api_key != DEFAULT_API_KEY:
+        return "🔒 Error: Invalid API Key"
+    
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+    
+    if history:
+        for user_msg, bot_msg in history:
+            messages.extend([
+                {"role": "user", "content": user_msg},
+                {"role": "assistant", "content": bot_msg}
+            ])
+    
     messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+    
+    try:
+        response = ""
+        for chunk in client.chat_completion(messages, stream=True):
+            response += chunk.choices[0].delta.content or ""
+        return response
+    except Exception as e:
+        return f"⚠️ Error: {str(e)}"
+
+# Gradio Interface
 demo = gr.ChatInterface(
-    respond,
+    fn=lambda msg, hist: generate_response(msg, hist, DEFAULT_API_KEY),
     additional_inputs=[
-        gr.Textbox(value="You are a chatbot that helps in mood detection of user and recommend them recipe based on the taste what they want ", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+        gr.Textbox(DEFAULT_API_KEY, label="API Key", type="password"),
+        gr.Textbox(SYSTEM_PROMPT, label="System Prompt")
     ],
+    title="🍳 Mood-to-Recipe Assistant",
+    description="Share your mood → Get personalized recipes!",
+    examples=[
+        ["I'm stressed and want comfort food"],
+        ["Feeling happy! Suggest something light and fresh"]
+    ]
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# For Hugging Face Spaces deployment
+demo.launch(show_api=True)