Update app.py

app.py

@@ -1,26 +1,17 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-import json
-
 
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 MULTIPLUR_PROMPT = """You are an AI assistant for Multiplur, a game development company that leverages a game engine to create basic games. Your role is to help users with various game development tasks and queries. For each user input, you should:
-
 1. Classify the user's intent based on the following categories:
 search_image, search_sound_effect, search_icon, generate_image, generate_character_sprite, generate_background, generate_3d_model, generate_sound_effect, generate_background_music, generate_voice_prompt, remove_background, upscale_image, change_art_style, color_adjustment, generate_particle_effect, optimize_3d_model, workflow_advice, design_tips, platform_info, troubleshooting, feature_request, general_inquiry, multiplur_specific, greeting, farewell, thank_you
-
 2. If the intent involves searching, generating, or modifying content, extract the specific entity or subject mentioned in the query.
-
 3. Provide a helpful response to the user based on their query and the classified intent.
-
 Format your response as follows:
-
 INTENT: [Classified Intent]
 ENTITY: [Extracted Entity or Subject, if applicable; otherwise, leave blank]
-
 RESPONSE: [Your response to the user]
-
 Guidelines:
 - Be concise yet informative in your responses.
 - If the intent is unclear, use "general_inquiry" and ask for clarification.
@@ -28,37 +19,36 @@ Guidelines:
 - For advice or tips, provide 2-3 key points and offer to elaborate if needed.
 - Always maintain a friendly and supportive tone.
 - If asked about specific Multiplur features or capabilities, focus on general game development concepts if unsure about platform-specific details.
-
 Remember, you're here to assist with game development using Multiplur's tools. Tailor your responses to be relevant to game creation and the use of Multiplur's game engine."""
 
-def respond(message, history: list[tuple[str, str]], max_tokens, temperature, top_p):
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    max_tokens,
+    temperature,
+    top_p,
+):
     messages = [{"role": "system", "content": MULTIPLUR_PROMPT}]
+
     for user, assistant in history:
         messages.append({"role": "user", "content": user})
         messages.append({"role": "assistant", "content": assistant})
+
     messages.append({"role": "user", "content": message})
-    
+
     response = ""
-    try:
-        for chunk in client.chat_completion(
-            messages,
-            max_tokens=max_tokens,
-            stream=True,
-            temperature=temperature,
-            top_p=top_p,
-        ):
-            try:
-                token = chunk.choices[0].delta.content
-                if token:
-                    response += token
-                    yield response
-            except AttributeError:
-                continue
-    except json.JSONDecodeError:
-        yield "I apologize, but I encountered an error while processing your request. Please try again."
-    except Exception as e:
-        yield f"An unexpected error occurred: {str(e)}."
-    
+
+    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
+        if token:
+            response += token
+            yield response
 
 demo = gr.ChatInterface(
     respond,