Update app.py

app.py

@@ -12,8 +12,8 @@ WHISPER_API_URL = "https://api-inference.huggingface.co/models/openai/whisper-la
 # Set up headers for the Whisper API request
 headers = {"Authorization": f"Bearer {api_key}"}
 
-# Load the DeepSeek model using Hugging Face's pipeline (no API call, local model)
-pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1", trust_remote_code=True)
+# Load the DeepSeek model using Gradio's load function from the registry
+demo = gr.load(name="deepseek-ai/DeepSeek-R1", src="transformers_gradio.registry")
 
 # Function to query the Hugging Face Whisper model for audio transcription (API call)
 def transcribe_audio(audio_file):
@@ -25,14 +25,14 @@ def transcribe_audio(audio_file):
     else:
         return f"Error: {response.status_code}, {response.text}"
 
-# Function to generate Mermaid.js code using DeepSeek-R1 model (local processing)
+# Function to generate Mermaid.js code using DeepSeek-R1 model
 def generate_mermaid_code(prompt):
     # Instruction included in the prompt to guide DeepSeek to generate valid MermaidJS code
-    deepseek_prompt = f"Generate all possible valid MermaidJS diagram code for the following: {prompt}"
+    deepseek_prompt = f"Generate a valid MermaidJS diagram code for the following: {prompt}"
     
-    # Using the DeepSeek model pipeline for text generation
-    response = pipe([{"role": "user", "content": deepseek_prompt}])
-    return response[0]["generated_text"].strip()
+    # Use the loaded model `demo` to generate the MermaidJS code
+    response = demo(deepseek_prompt)
+    return response.strip()
 
 # Function to process text, audio, or both inputs
 def process_input(input_type, text_input, audio_input):