Update app.py

app.py

@@ -8,38 +8,18 @@ pipe = pipeline("visual-question-answering", model="dandelin/vilt-b32-finetuned-
 # Load model directly
 model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large", trust_remote_code=True)
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 def respond(message, history, system_message, max_tokens, temperature, top_p):
-    """
-    Generates a response based on the user message and chat history.
-    
-    Args:
-        message (str): The user message.
-        history (list): A list of tuples containing user and assistant messages.
-        system_message (str): The system message.
-        max_tokens (int): Maximum number of tokens for the response.
-        temperature (float): Temperature for the response generation.
-        top_p (float): Top-p for nucleus sampling.
-    
-    Yields:
-        str: The generated response.
-    """
     messages = [{"role": "system", "content": system_message}]
-
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
-
     messages.append({"role": "user", "content": message})
 
     response = ""
-
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -52,50 +32,20 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
         yield response
 
 def process_video(video):
-    """
-    Processes the uploaded video file.
-    
-    Args:
-        video (gr.Video): The uploaded video file.
-    
-    Returns:
-        str: Confirmation message for the uploaded video.
-    """
     return f"Processing video: {video.name}"
 
 def process_pdf(pdf):
-    """
-    Processes the uploaded PDF file.
-    
-    Args:
-        pdf (gr.File): The uploaded PDF file.
-    
-    Returns:
-        str: Confirmation message for the uploaded PDF.
-    """
     return f"Processing PDF: {pdf.name}"
 
 def process_image(image):
-    """
-    Processes the uploaded image file.
-    
-    Args:
-        image (gr.Image): The uploaded image file.
-    
-    Returns:
-        str: Confirmation message for the uploaded image.
-    """
     return f"Processing image: {image.name}"
 
-# Define upload interfaces
 video_upload = gr.Interface(fn=process_video, inputs=gr.Video(), outputs="text", title="Upload a Video")
 pdf_upload = gr.Interface(fn=process_pdf, inputs=gr.File(file_types=['.pdf']), outputs="text", title="Upload a PDF")
 image_upload = gr.Interface(fn=process_image, inputs=gr.Image(), outputs="text", title="Upload an Image")
 
-# Combine upload interfaces into tabs
 tabbed_interface = gr.TabbedInterface([video_upload, pdf_upload, image_upload], ["Video", "PDF", "Image"])
 
-# Main Gradio interface
 demo = gr.Blocks()
 
 with demo: