Update app.py

app.py

@@ -2,15 +2,14 @@ import gradio as gr
 import torch
 from transformers import AutoModel, AutoTokenizer
 
-# Set the model path (this is the repository/model ID on Hugging Face)
+# Model setting
 model_path = "OpenGVLab/InternVideo2_5_Chat_8B"
 
 # Load the tokenizer and model with remote code enabled.
-# .half() converts the model to FP16 and .cuda() moves it to GPU (if available).
 tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
 model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()
 
-# Get the image processor from the vision tower (if needed by the model's implementation)
+# Get the image processor from the vision tower.
 image_processor = model.get_vision_tower().image_processor
 
 # Evaluation settings
@@ -23,23 +22,37 @@ generation_config = {
     "num_beams": 1,
 }
 
-# Define a chat function that performs either single-turn or multi-turn conversation.
+video_path = "your_video.mp4"  # (For testing locally, update as needed)
+
+# Single-turn conversation example:
+def single_turn_chat(video_path, user_prompt):
+    output, chat_history = model.chat(
+        video_path=video_path,
+        tokenizer=tokenizer,
+        user_prompt=user_prompt,
+        return_history=True,
+        max_num_frames=max_num_frames,
+        generation_config=generation_config
+    )
+    return output
+
+# Multi-turn conversation example:
+def multi_turn_chat(video_path, user_prompt, chat_history):
+    output, chat_history = model.chat(
+        video_path=video_path,
+        tokenizer=tokenizer,
+        user_prompt=user_prompt,
+        chat_history=chat_history,
+        return_history=True,
+        max_num_frames=max_num_frames,
+        generation_config=generation_config
+    )
+    return output, chat_history
+
+# For the Gradio interface, we'll combine these into a chat function.
 def chat_interface(video_path, user_prompt, chat_history):
-    """
-    Performs a chat turn with the model. If no chat_history is provided,
-    it starts a new conversation.
-    
-    Parameters:
-        video_path (str): The filepath of the uploaded video.
-        user_prompt (str): The user's question.
-        chat_history (list): The conversation history (empty list for a new conversation).
-    
-    Returns:
-        A tuple containing the model's output (str) and the updated chat history (list).
-    """
     if chat_history is None:
         chat_history = []
-    # The model.chat() method returns output and updated history.
     output, new_history = model.chat(
         video_path=video_path,
         tokenizer=tokenizer,
@@ -57,21 +70,15 @@ with gr.Blocks() as demo:
     with gr.Row():
         video_input = gr.Video(label="Upload Video", type="filepath")
         question_input = gr.Textbox(label="Enter your question", placeholder="Type your question here...")
-    # We'll use a hidden state to keep the conversation history.
-    chat_state = gr.State([])
+    chat_state = gr.State([])  # To maintain conversation history
     output_text = gr.Textbox(label="Model Response")
     
-    def process_chat(video, question, history):
-        response, new_history = chat_interface(video, question, history)
-        return response, new_history
-
     send_btn = gr.Button("Send")
     send_btn.click(
-        process_chat,
+        chat_interface,
         inputs=[video_input, question_input, chat_state],
         outputs=[output_text, chat_state]
     )
 
-# Launch the app.
 if __name__ == "__main__":
     demo.launch()