Update app.py

app.py

@@ -1,21 +1,47 @@
 from huggingface_hub import InferenceClient
 import os
 api_key=os.getenv("HF_TOKEN")
+import gradio as gr
+from huggingface_hub import InferenceClient
+import os
+
+# Initialize Hugging Face Inference Client
+api_key = os.getenv("HF_TOKEN")
 client = InferenceClient(api_key=api_key)
 
-messages = [
-    { "role": "system", "content": "You are a good image generation prompt engineer for diffuser image generation models" },
-	{ "role": "user", "content": "Visualy describe a random character in extreme detail" }
-]
+def chat_with_model(user_input):
+    """Send user input to the model and return its response."""
+    messages = [
+        {"role": "system", "content": "You are a good image generation prompt engineer for diffuser image generation models"},
+        {"role": "user", "content": user_input}
+    ]
+
+    try:
+        result = client.chat.completions.create(
+            model="HuggingFaceH4/zephyr-7b-beta",
+            messages=messages,
+            temperature=0.5,
+            max_tokens=2048,
+            top_p=0.7,
+            stream=False  # Stream disabled for simplicity
+        )
+        return result["choices"][0]["message"]["content"]
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Hugging Face Chatbot with Gradio")
+
+    with gr.Row():
+        with gr.Column():
+            user_input = gr.Textbox(label="Enter your prompt", placeholder="Describe the character or request a detailed description...")
+            submit_button = gr.Button("Generate")
+        
+        with gr.Column():
+            output = gr.Textbox(label="Model Response", interactive=False)
+
+    submit_button.click(chat_with_model, inputs=[user_input], outputs=[output])
 
-result = client.chat.completions.create(
-    model="HuggingFaceH4/zephyr-7b-beta", 
-	messages=messages, 
-	temperature=0.5,
-	max_tokens=2048,
-	top_p=0.7,
-	stream=True
-)
-text=result#.choices[0].delta.content
-#for chunk in stream:
-    #print(chunk.choices[0].delta.content)
+# Run the app
+demo.launch()