Update app.py

app.py

@@ -1,96 +1,7 @@
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from transformers import pipeline, set_seed
 
-# CPU-friendly model settings
-# Using a smaller model with quantization for CPU compatibility
-model_name = "describeai/gemini"  # Smaller 2B parameter model
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+summarizer = pipeline('text2text-generation', model='describeai/gemini')
+code = "print('hello world!')"
 
-# Configure quantization for better CPU performance
-quantization_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_compute_dtype=torch.float16
-)
-
-# Load model with CPU optimizations
-try:
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name,
-        quantization_config=quantization_config,
-        device_map="auto"  # Will use CPU if no GPU is available
-    )
-    using_quantization = True
-except Exception as e:
-    print(f"Quantization failed with error: {e}")
-    print("Falling back to standard CPU loading...")
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name,
-        torch_dtype=torch.float32,  # Use float32 for CPU
-        device_map="cpu"  # Explicitly use CPU
-    )
-    using_quantization = False
-
-print(f"Model loaded on CPU. Using quantization: {using_quantization}")
-print(f"Model size: {model_name}")
-
-# Define a function to generate text with adjusted parameters for CPU
-def generate_response(prompt, max_length=200):  # Reduced max length
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    
-    print("Generating response (this may take a while on CPU)...")
-    start_time = time.time()
-    
-    # Generate output with more conservative settings for CPU
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=max_length,
-        do_sample=False,  # Deterministic generation is faster
-        num_beams=1,      # No beam search for speed
-    )
-    
-    end_time = time.time()
-    print(f"Generation completed in {end_time - start_time:.2f} seconds")
-    
-    # Decode and return the generated text
-    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
-    # Remove the prompt from the response
-    return generated_text[len(tokenizer.decode(inputs.input_ids[0], skip_special_tokens=True)):]
-
-# Test the model with simpler, shorter prompts for CPU evaluation
-import time
-
-test_prompts = [
-    "Explain what machine learning is in one paragraph.",
-    "Write a haiku about computers.",
-    "List three benefits of open-source software."
-]
-
-# Run evaluation
-print("\nEvaluating Gemini open source model on CPU\n")
-print("=" * 50)
-
-for i, prompt in enumerate(test_prompts):
-    print(f"\nPrompt {i+1}: {prompt}")
-    print("-" * 50)
-    
-    start_time = time.time()
-    response = generate_response(prompt)
-    end_time = time.time()
-    
-    print(f"Response time: {end_time - start_time:.2f} seconds")
-    print(f"Response:\n{response}")
-    print("=" * 50)
-
-# Memory usage information
-import psutil
-process = psutil.Process()
-memory_info = process.memory_info()
-print(f"\nMemory Usage: {memory_info.rss / (1024 * 1024):.2f} MB")
-
-# Save model output to a file for later analysis
-with open("gemini_cpu_evaluation_results.txt", "w") as f:
-    f.write("GEMINI MODEL CPU EVALUATION RESULTS\n\n")
-    for i, prompt in enumerate(test_prompts):
-        f.write(f"Prompt {i+1}: {prompt}\n")
-        f.write(f"Response:\n{generate_response(prompt)}\n\n")
+response = summarizer(code, max_length=100, num_beams=3)
+print("Summarized code: " + response[0]['generated_text'])