Update app.py

app.py

@@ -1,21 +1,21 @@
 import gradio as gr
-from transformers import pipeline
-import os
 import requests
+import os
+from transformers import pipeline
 
-# Set up the Hugging Face API key for Whisper
+# Set up the Hugging Face API key (ensure you've set this as an environment variable)
 api_key = os.getenv("HUGGINGFACEHUB_API_TOKEN")
 
-# Set up the API URL for Whisper
+# API URL for Whisper (audio transcription)
 WHISPER_API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large-v3-turbo"
 
 # Set up headers for the Whisper API request
 headers = {"Authorization": f"Bearer {api_key}"}
 
-# Load the DeepSeek model using Gradio's load function from the registry
-demo = gr.load(name="deepseek-ai/DeepSeek-R1", src="transformers_gradio.registry")
+# Load the DeepSeek model using Hugging Face's pipeline
+pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
 
-# Function to query the Hugging Face Whisper model for audio transcription (API call)
+# Function to query the Hugging Face Whisper model for audio transcription
 def transcribe_audio(audio_file):
     with open(audio_file, "rb") as f:
         data = f.read()
@@ -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
+# Function to generate Mermaid.js code using the DeepSeek model (replaces Mistral API call)
 def generate_mermaid_code(prompt):
     # Instruction included in the prompt to guide DeepSeek to generate valid MermaidJS code
     deepseek_prompt = f"Generate a valid MermaidJS diagram code for the following: {prompt}"
     
-    # Use the loaded model `demo` to generate the MermaidJS code
-    response = demo(deepseek_prompt)
-    return response.strip()
+    # Using the DeepSeek model pipeline for text generation
+    response = pipe([{"role": "user", "content": deepseek_prompt}])
+    return response[0]["generated_text"].strip()
 
 # Function to process text, audio, or both inputs
 def process_input(input_type, text_input, audio_input):