Update app.py

app.py

@@ -1,282 +1,93 @@
 import gradio as gr
 import torch
-import cv2
-import numpy as np
-from PIL import Image
-import spaces
-import tempfile
-import os
-import subprocess
-import sys
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-# Install dependencies if needed
-def install_dependencies():
-    """Install required packages for VideoLLaMA3"""
-    packages = ["decord", "imageio", "imageio-ffmpeg"]
-    for package in packages:
-        try:
-            __import__(package.replace("-", "_"))
-        except ImportError:
-            print(f"Installing {package}...")
-            subprocess.check_call([sys.executable, "-m", "pip", "install", package, "--quiet"])
+# HERE IS WHERE THE MODEL NAME GOES ⬇️
+model_name = "DAMO-NLP-SG/VideoRefer-VideoLLaMA3-7B"
 
-# Install dependencies on startup
-install_dependencies()
-
-from transformers import AutoModelForCausalLM, AutoProcessor
-import warnings
-warnings.filterwarnings("ignore")
-
-# Global variables
-model = None
-processor = None
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_loaded = False
-
-@spaces.GPU
-def load_videollama3_model():
-    """Load VideoLLaMA3 model with the correct implementation"""
-    global model, processor, model_loaded
-    
+# Load the model function
+def load_model():
     try:
-        print("🔄 Loading VideoLLaMA3-7B model...")
-        
-        model_path = "DAMO-NLP-SG/VideoLLaMA3-7B"
-        
-        print("Loading processor...")
-        processor = AutoProcessor.from_pretrained(
-            model_path, 
-            trust_remote_code=True
-        )
-        
-        print("Loading VideoLLaMA3 model (this may take several minutes)...")
+        # Use the model name here ⬇️
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
         model = AutoModelForCausalLM.from_pretrained(
-            model_path,
-            trust_remote_code=True,
-            device_map={"": device},
-            torch_dtype=torch.bfloat16,
+            model_name,  # And here ⬇️
+            torch_dtype=torch.float16,
+            device_map="auto",
+            trust_remote_code=True  # May be needed for some models
         )
-        
-        model_loaded = True
-        success_msg = "✅ VideoLLaMA3-7B model loaded successfully! Ready for video analysis."
-        print(success_msg)
-        return success_msg
-        
+        return tokenizer, model
     except Exception as e:
-        model_loaded = False
-        error_msg = f"❌ Failed to load VideoLLaMA3: {str(e)}"
-        print(error_msg)
-        return error_msg
+        return None, None
+
+# Initialize model (this happens when the Space starts)
+print(f"Loading model: {model_name}")  # And you can use it here ⬇️
+tokenizer, model = load_model()
 
-@spaces.GPU
-def analyze_video_with_videollama3(video_file, question, progress=gr.Progress()):
-    """Analyze video using VideoLLaMA3 - REAL implementation"""
+def process_video_question(video_file, question):
+    """Process video and answer questions about it"""
+    if model is None:
+        return "Sorry, the model failed to load. Please try again later."
     
     if video_file is None:
-        return "❌ Please upload a video file first."
+        return "Please upload a video file first."
     
     if not question.strip():
-        return "❌ Please enter a question about the video."
-    
-    if not model_loaded or model is None or processor is None:
-        return "❌ VideoLLaMA3 model is not loaded. Please click 'Load VideoLLaMA3 Model' first and wait for completion."
+        return "Please enter a question about the video."
     
     try:
-        progress(0.1, desc="Preparing video for VideoLLaMA3...")
-        
-        # Create the exact conversation format from VideoLLaMA3 official implementation
-        conversation = [
-            {"role": "system", "content": "You are a helpful assistant."},
-            {
-                "role": "user", 
-                "content": [
-                    {"type": "video", "video": {"video_path": video_file, "fps": 1, "max_frames": 128}},
-                    {"type": "text", "text": question}
-                ]
-            }
-        ]
-        
-        progress(0.3, desc="Processing with VideoLLaMA3...")
-        
-        # Use the EXACT processor call from official VideoLLaMA3 code
-        inputs = processor(
-            conversation=conversation,
-            add_system_prompt=True,
-            add_generation_prompt=True,
-            return_tensors="pt"
-        )
-        
-        # Move inputs to device
-        inputs = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
-        
-        if "pixel_values" in inputs:
-            inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
-        
-        progress(0.7, desc="Generating VideoLLaMA3 response...")
-        
-        # Generate response with VideoLLaMA3
-        with torch.no_grad():
-            output_ids = model.generate(
-                **inputs,
-                max_new_tokens=512,
-                do_sample=True,
-                temperature=0.1,
-                use_cache=True,
-                pad_token_id=processor.tokenizer.eos_token_id,
-                eos_token_id=processor.tokenizer.eos_token_id
-            )
-        
-        progress(0.9, desc="Processing VideoLLaMA3 response...")
-        
-        # Decode the response
-        response = processor.batch_decode(output_ids, skip_special_tokens=True)[0]
-        
-        # Extract assistant response - VideoLLaMA3 specific parsing
-        if "assistant" in response.lower():
-            ai_response = response.split("assistant")[-1].strip()
-        elif "<|im_start|>assistant" in response:
-            ai_response = response.split("<|im_start|>assistant")[-1].strip()
-        else:
-            # Fallback: extract everything after the user's question
-            parts = response.split(question)
-            if len(parts) > 1:
-                ai_response = parts[-1].strip()
-            else:
-                ai_response = response.strip()
+        # Your video processing logic would go here
+        # This is a placeholder - you'll need to implement the actual VideoLLaMA3 pipeline
         
-        # Clean up response tokens
-        ai_response = ai_response.replace("<|im_end|>", "").replace("</s>", "").strip()
-        
-        # Get video metadata
-        cap = cv2.VideoCapture(video_file)
-        fps = cap.get(cv2.CAP_PROP_FPS)
-        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-        duration = total_frames / fps if fps > 0 else 0
-        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        cap.release()
-        
-        progress(1.0, desc="VideoLLaMA3 analysis complete!")
-        
-        # Format response
-        formatted_response = f"""🎥 **VideoLLaMA3 AI Video Analysis**
-
-❓ **Your Question:** 
-{question}
-
-🤖 **VideoLLaMA3 Response:**
-{ai_response}
-
-📊 **Video Details:**
-• Duration: {duration:.1f} seconds
-• Resolution: {width}x{height}
-• Frame Rate: {fps:.1f} FPS  
-• Total Frames: {total_frames:,}
-• Analyzed with: Up to 128 frames at 1 FPS
-
-⚡ **Powered by:** VideoLLaMA3-7B (Official Implementation)
-"""
-        
-        return formatted_response
+        # For now, just return a simple response
+        response = f"I received your video and question: '{question}'. Video processing with {model_name} would happen here."
+        return response
         
     except Exception as e:
-        error_msg = f"❌ VideoLLaMA3 analysis failed: {str(e)}"
-        print(f"Full error: {e}")
-        return error_msg
+        return f"Error processing video: {str(e)}"
 
-def create_interface():
-    """Create the VideoLLaMA3 interface"""
+# Create the Gradio interface
+with gr.Blocks(title="VideoLLaMA3 Demo") as demo:
+    gr.Markdown("# 🎥 VideoLLaMA3 Interactive Demo")
+    gr.Markdown(f"**Model:** `{model_name}`")  # Display the model name ⬇️
+    gr.Markdown("Upload a video and ask questions about its content!")
     
-    with gr.Blocks(title="VideoLLaMA3 Official", theme=gr.themes.Soft()) as demo:
-        gr.Markdown("# 🎥 VideoLLaMA3 Video Analysis Tool")
-        gr.Markdown("**Official VideoLLaMA3-7B implementation** - Upload videos and get detailed AI analysis!")
-        
-        # Model loading section
-        with gr.Row():
-            with gr.Column(scale=3):
-                model_status = gr.Textbox(
-                    label="🤖 VideoLLaMA3 Model Status", 
-                    value="Model not loaded - Click button to load VideoLLaMA3-7B →",
-                    interactive=False,
-                    lines=2
-                )
-            with gr.Column(scale=1):
-                load_btn = gr.Button("🚀 Load VideoLLaMA3", variant="primary", size="lg")
-        
-        load_btn.click(load_videollama3_model, outputs=model_status)
-        
-        gr.Markdown("---")
-        
-        # Main interface
-        with gr.Row():
-            with gr.Column(scale=1):
-                video_input = gr.Video(
-                    label="📹 Upload Video (MP4, AVI, MOV, WebM)",
-                    height=350
-                )
-                question_input = gr.Textbox(
-                    label="❓ Ask VideoLLaMA3 about the video",
-                    placeholder="What is happening in this video? Describe it in detail.",
-                    lines=3,
-                    max_lines=5
-                )
-                analyze_btn = gr.Button("🔍 Analyze with VideoLLaMA3", variant="primary", size="lg")
-                
-            with gr.Column(scale=1):
-                output = gr.Textbox(
-                    label="🎯 VideoLLaMA3 Analysis Results",
-                    lines=25,
-                    max_lines=30,
-                    show_copy_button=True
-                )
-        
-        # Example questions
-        gr.Markdown("### 💡 Example Questions for VideoLLaMA3:")
-        
-        example_questions = [
-            "What is happening in this video? Describe the scene in detail.",
-            "Who are the people in this video and what are they doing?",
-            "Describe the setting, location, and environment shown.",
-            "What objects can you identify in this video?",
-            "What is the mood or atmosphere of this video?",
-            "Can you summarize the key events chronologically?"
-        ]
-        
-        with gr.Row():
-            for i in range(0, len(example_questions), 2):
-                with gr.Column():
-                    if i < len(example_questions):
-                        btn1 = gr.Button(example_questions[i], size="sm")
-                        btn1.click(lambda x=example_questions[i]: x, outputs=question_input)
-                    if i+1 < len(example_questions):
-                        btn2 = gr.Button(example_questions[i+1], size="sm") 
-                        btn2.click(lambda x=example_questions[i+1]: x, outputs=question_input)
-        
-        # Connect analyze button
-        analyze_btn.click(
-            analyze_video_with_videollama3,
-            inputs=[video_input, question_input],
-            outputs=output,
-            show_progress=True
-        )
-        
-        gr.Markdown("---")
-        gr.Markdown("""
-        ### 📋 How to Use VideoLLaMA3:
-        1. **Load Model:** Click "Load VideoLLaMA3" and wait (~10 minutes for first load)
-        2. **Upload Video:** Choose your video file (works best under 2 minutes)  
-        3. **Ask Question:** Type what you want to know about the video
-        4. **Analyze:** Click "Analyze with VideoLLaMA3" for AI-powered insights
-        
-        ### 🔧 Technical Details:
-        - **Model:** VideoLLaMA3-7B (Official DAMO-NLP-SG implementation)
-        - **Analysis:** Processes up to 128 frames at 1 FPS sampling
-        - **Capabilities:** Video understanding, object detection, scene description, temporal reasoning
-        - **Best Performance:** Videos under 2 minutes, clear visuals, specific questions
-        """)
+    with gr.Row():
+        with gr.Column(scale=1):
+            video_input = gr.Video(
+                label="📹 Upload Video",
+                height=300
+            )
+            question_input = gr.Textbox(
+                label="❓ Ask a question about the video",
+                placeholder="What is happening in this video?",
+                lines=2
+            )
+            submit_btn = gr.Button("🚀 Analyze Video", variant="primary")
+            
+        with gr.Column(scale=1):
+            output_text = gr.Textbox(
+                label="🤖 AI Response",
+                lines=10,
+                placeholder="The AI response will appear here..."
+            )
+    
+    # Examples section
+    gr.Markdown("### 💡 Example Questions:")
+    gr.Markdown("""
+    - "What objects can you see in this video?"
+    - "Describe the main action happening"
+    - "What is the setting or location?"
+    - "How many people are in the video?"
+    """)
     
-    return demo
+    # Connect the button to the function
+    submit_btn.click(
+        fn=process_video_question,
+        inputs=[video_input, question_input],
+        outputs=output_text
+    )
 
+# Launch the app
 if __name__ == "__main__":
-    demo = create_interface()
     demo.launch()