Update app.py

app.py

@@ -1,248 +1,40 @@
 import gradio as gr
 import torch
-from transformers import AutoProcessor, AutoModelForCausalLM, AutoTokenizer
-import time
-import os
-from huggingface_hub import login
-import requests
-from PIL import Image
-from io import BytesIO
-import base64
-import traceback
 
-# Function to run the Qwen text test
-def test_text_generation():
-    results = []
-    results.append("=== Testing Qwen Text Generation ===")
+# Create a simple interface
+with gr.Blocks(title="Simple Qwen Test") as demo:
+    gr.Markdown("# Simple Qwen Test")
+    gr.Markdown("This is a minimal test to check if the Space is working.")
     
-    try:
-        # Get token from environment
-        token = os.environ.get("HUGGINGFACE_TOKEN", "")
-        if token:
-            results.append(f"Token found: {token[:5]}...")
-        else:
-            results.append("No token found in environment variables!")
-            return "\n".join(results)
-        
-        # Login to Hugging Face
-        try:
-            login(token=token)
-            results.append("Successfully logged in to Hugging Face Hub")
-        except Exception as e:
-            results.append(f"Error logging in: {e}")
-            return "\n".join(results)
-        
-        # Use Qwen model
-        model_id = "Qwen/Qwen2-7B-Instruct"  # Smaller model for testing
-        
-        results.append(f"Loading tokenizer from {model_id}...")
-        from transformers import AutoTokenizer, AutoModelForCausalLM
-        
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        
-        results.append(f"Loading model from {model_id}...")
-        model = AutoModelForCausalLM.from_pretrained(
-            model_id,
-            torch_dtype=torch.bfloat16,
-            device_map="auto"
-        )
-        
-        results.append("Model and tokenizer loaded successfully!")
-        
-        # Simple prompt
-        prompt = "Write a short poem about artificial intelligence."
-        
-        results.append(f"Generating text for prompt: '{prompt}'")
-        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-        
-        start_time = time.time()
-        outputs = model.generate(**inputs, max_new_tokens=100)
-        end_time = time.time()
-        
-        result = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        
-        results.append(f"Generation completed in {end_time - start_time:.2f} seconds")
-        results.append(f"Result: {result}")
-        results.append("TEXT TEST SUCCESSFUL!")
-        
-    except Exception as e:
-        results.append(f"Error in text generation test: {e}")
-        results.append(traceback.format_exc())
-        results.append("TEXT TEST FAILED!")
-    
-    return "\n".join(results)
-
-# Function to run the Qwen image-text test
-def test_image_text_generation():
-    results = []
-    results.append("=== Testing Qwen Image-Text Generation ===")
-    
-    try:
-        # Get token from environment
-        token = os.environ.get("HUGGINGFACE_TOKEN", "")
-        if token:
-            results.append(f"Token found: {token[:5]}...")
-        else:
-            results.append("No token found in environment variables!")
-            return "\n".join(results)
-        
-        # Login to Hugging Face
-        try:
-            login(token=token)
-            results.append("Successfully logged in to Hugging Face Hub")
-        except Exception as e:
-            results.append(f"Error logging in: {e}")
-            return "\n".join(results)
-        
-        # Use the Qwen VL model
-        model_id = "Qwen/Qwen2-VL-7B"  # Visual language model
-        
-        results.append(f"Loading processor and model from {model_id}...")
-        
-        # Load processor and model with the correct classes
-        from transformers import Qwen2VLProcessor, Qwen2VLForConditionalGeneration
-        
-        processor = Qwen2VLProcessor.from_pretrained(model_id)
-        model = Qwen2VLForConditionalGeneration.from_pretrained(
-            model_id,
-            torch_dtype=torch.bfloat16,
-            device_map="auto"
-        )
-        
-        results.append("Model and processor loaded successfully!")
-        
-        # Load a test image
-        results.append("Loading test image...")
-        response = requests.get("https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg")
-        img = Image.open(BytesIO(response.content))
-        results.append(f"Image loaded: {img.size}")
-        
-        # Simple prompt
-        prompt = "Describe this image in two sentences."
-        
-        results.append(f"Creating prompt with image: '{prompt}'")
-        
-        # Process inputs
-        inputs = processor(text=prompt, images=img, return_tensors="pt").to(model.device)
-        
-        results.append("Generating response...")
-        start_time = time.time()
-        outputs = model.generate(**inputs, max_new_tokens=100)
-        end_time = time.time()
-        
-        result = processor.decode(outputs[0], skip_special_tokens=True)
-        
-        results.append(f"Generation completed in {end_time - start_time:.2f} seconds")
-        results.append(f"Result: {result}")
-        results.append("IMAGE-TEXT TEST SUCCESSFUL!")
-        
-    except Exception as e:
-        results.append(f"Error in image-text generation test: {e}")
-        results.append(traceback.format_exc())
-        results.append("IMAGE-TEXT TEST FAILED!")
-    
-    return "\n".join(results)
-
-# Function to list available Qwen models
-def list_qwen_models():
-    results = []
-    results.append("=== Listing Available Qwen Models ===")
-    
-    try:
-        # Get token from environment
-        token = os.environ.get("HUGGINGFACE_TOKEN", "")
-        if token:
-            results.append(f"Token found: {token[:5]}...")
-        else:
-            results.append("No token found in environment variables!")
-            return "\n".join(results)
-        
-        # Login to Hugging Face
-        try:
-            login(token=token)
-            results.append("Successfully logged in to Hugging Face Hub")
-        except Exception as e:
-            results.append(f"Error logging in: {e}")
-            return "\n".join(results)
-        
-        # List models from the Qwen organization
-        from huggingface_hub import HfApi
-        api = HfApi(token=token)
-        
-        results.append("Fetching models from Qwen organization...")
-        results.append("Available Qwen models:")
-        
-        model_count = 0
-        for model in api.list_models(author="Qwen"):
-            results.append(f"- {model.id}")
-            model_count += 1
-        
-        results.append(f"Total Qwen models found: {model_count}")
-        results.append("MODEL LISTING SUCCESSFUL!")
-        
-    except Exception as e:
-        results.append(f"Error listing models: {e}")
-        results.append(traceback.format_exc())
-        results.append("MODEL LISTING FAILED!")
-    
-    return "\n".join(results)
-
-# Create Gradio interface
-with gr.Blocks(title="Qwen Model Test") as demo:
-    gr.Markdown("# Qwen Model Test")
-    gr.Markdown("This Space tests the connection to Qwen models.")
-    
-    with gr.Tab("List Qwen Models"):
+    with gr.Tab("Basic Test"):
         with gr.Row():
             with gr.Column():
-                list_models_button = gr.Button("List Qwen Models")
+                test_button = gr.Button("Run Basic Test")
             with gr.Column():
-                models_result = gr.Textbox(label="Available Qwen Models", lines=20)
-        
-        list_models_button.click(
-            fn=list_qwen_models,
+                test_result = gr.Textbox(label="Test Results", lines=10)
+        
+        def basic_test():
+            try:
+                # Just print system info
+                import sys
+                import transformers
+                
+                result = []
+                result.append(f"Python version: {sys.version}")
+                result.append(f"PyTorch version: {torch.__version__}")
+                result.append(f"Transformers version: {transformers.__version__}")
+                result.append("Basic test successful!")
+                
+                return "\n".join(result)
+            except Exception as e:
+                import traceback
+                return f"Error: {str(e)}\n\n{traceback.format_exc()}"
+        
+        test_button.click(
+            fn=basic_test,
             inputs=[],
-            outputs=[models_result]
+            outputs=[test_result]
         )
-    
-    with gr.Tab("Text Generation Test"):
-        with gr.Row():
-            with gr.Column():
-                text_test_button = gr.Button("Run Text Generation Test")
-            with gr.Column():
-                text_test_result = gr.Textbox(label="Test Results", lines=20)
-        
-        text_test_button.click(
-            fn=test_text_generation,
-            inputs=[],
-            outputs=[text_test_result]
-        )
-    
-    with gr.Tab("Image-Text Generation Test"):
-        with gr.Row():
-            with gr.Column():
-                image_test_button = gr.Button("Run Image-Text Generation Test")
-            with gr.Column():
-                image_test_result = gr.Textbox(label="Test Results", lines=20)
-        
-        image_test_button.click(
-            fn=test_image_text_generation,
-            inputs=[],
-            outputs=[image_test_result]
-        )
-    
-    with gr.Tab("About"):
-        gr.Markdown("""
-        ## About This Test
-        
-        This test checks if your Space can connect to and use Qwen models.
-        
-        - The **List Qwen Models** tab shows all models available from Qwen
-        - The **Text Generation Test** uses Qwen2-7B-Instruct for basic text generation
-        - The **Image-Text Generation Test** uses Qwen2-VL-7B for image-text generation
-        
-        If both tests pass, your Qwen setup should work correctly.
-        """)
 
 # Launch the app
 demo.launch()