Update app.py

app.py

@@ -118,12 +118,12 @@ AVAILABLE_MODELS = [
     {
         "name": "DeepSeek V3",
         "id": "deepseek-ai/DeepSeek-V3-0324",
-        "description": "DeepSeek V3 model for code generation", "provider": "huggingface"
+        "description": "DeepSeek V3 model for code generation", "provider": "deepseek"
     },
     {
         "name": "DeepSeek R1",
         "id": "deepseek-ai/DeepSeek-R1-0528",
-        "description": "DeepSeek R1 model for code generation", "provider": "huggingface"
+        "description": "DeepSeek R1 model for code generation", "provider": "deepseek"
     },
     {
     #    "name": "GPT-4o-mini",
@@ -216,6 +216,7 @@ DEMO_LIST = [
 HF_TOKEN = os.getenv("HF_TOKEN")
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
 
 def get_inference_client(model_id):
    """Return an appropriate client based on model_id and configured provider."""
@@ -223,7 +224,13 @@ def get_inference_client(model_id):
    if not model_info:
        raise ValueError(f"Model with id '{model_id}' not found.")
 
-   provider = model_info.get("provider", "huggingface")  # Default to HF
+   provider = model_info.get("provider")
+
+   if provider == "deepseek":
+       if not DEEPSEEK_API_KEY:
+           raise ValueError("DEEPSEEK_API_KEY environment variable not set.")
+       from deepseek.client import DeepSeekClient
+       return DeepSeekClient(api_key=DEEPSEEK_API_KEY)
 
    if provider == "groq":
        return InferenceClient(
@@ -277,7 +284,7 @@ def history_to_messages(history: History, system: str) -> Messages:
             text_content = ""
             for item in user_content:
                 if isinstance(item, dict) and item.get("type") == "text":
-                    text_content += item.get("text", "")
+                    text_content += str(item.get("text", ""))
             user_content = text_content if text_content else str(user_content)
         
         messages.append({'role': 'user', 'content': user_content})
@@ -294,7 +301,7 @@ def messages_to_history(messages: Messages) -> Tuple[str, History]:
             text_content = ""
             for item in user_content:
                 if isinstance(item, dict) and item.get("type") == "text":
-                    text_content += item.get("text", "")
+                    text_content += str(item.get("text", ""))
             user_content = text_content if text_content else str(user_content)
         
         history.append([user_content, r['content']])
@@ -309,7 +316,7 @@ def history_to_chatbot_messages(history: History) -> List[Dict[str, str]]:
             text_content = ""
             for item in user_msg:
                 if isinstance(item, dict) and item.get("type") == "text":
-                    text_content += item.get("text", "")
+                    text_content += str(item.get("text", ""))
             user_msg = text_content if text_content else str(user_msg)
         
         messages.append({"role": "user", "content": user_msg})
@@ -1079,6 +1086,27 @@ This will help me create a better design for you."""
                        history_output: history_to_chatbot_messages(_history),
                        sandbox: send_to_sandbox(clean_code) if language == "html" else "<div style='padding:1em;color:#888;text-align:center;'>Preview is only available for HTML. Please download your code using the download button above.</div>",
                    }
+        elif _current_model["provider"] == "deepseek":
+            # Use Deepseek client
+            stream = client.chat.completions.create(
+                model=_current_model["id"],
+                messages=[{"role": m["role"], "content": m["content"]} for m in messages],
+                stream=True,
+                max_tokens=5000,
+            )
+
+            content = ""
+            for chunk in stream:
+                if chunk.choices[0].delta.content:
+                    content += chunk.choices[0].delta.content
+                    clean_code = remove_code_block(content)
+                    # The logic here is the same as for OpenAI, can be refactored
+                    yield {
+                        code_output: gr.update(value=clean_code, language=get_gradio_language(language)),
+                        history_output: history_to_chatbot_messages(_history),
+                        sandbox: send_to_sandbox(clean_code) if language == "html" else "<div style='padding:1em;color:#888;text-align:center;'>Preview is only available for HTML. Please download your code using the download button above.</div>",
+                    }
+
         else:
             # Use Hugging Face Inference Client (or Groq) for other models
             completion = client.chat.completions.create(