Create app.py

app.py

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+import os
+import subprocess
+
+# Ensure SentencePiece and Accelerate are installed
+try:
+    import sentencepiece
+except ImportError:
+    subprocess.check_call([os.sys.executable, "-m", "pip", "install", "sentencepiece"])
+
+try:
+    import accelerate
+except ImportError:
+    subprocess.check_call([os.sys.executable, "-m", "pip", "install", "accelerate"])
+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Check if a GPU is available, otherwise use CPU
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load a smaller, faster model and tokenizer
+model_name = "distilgpt2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+
+# Function to evaluate the prompt using the loaded model
+def evaluar_prompt(prompt):
+    try:
+        # Generate analysis using the model
+        inputs = tokenizer(prompt, return_tensors="pt").to(device)
+        outputs = model.generate(inputs["input_ids"], max_length=150)  # Limit max_length for faster results
+        analysis = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Basic logic to guide the analysis based on critical thinking principles
+        feedback = "Análisis del Prompt:\n"
+        
+        # Check clarity
+        if len(prompt.split()) < 5:
+            feedback += "- Claridad: El prompt es muy breve y puede