Update app.py

app.py

@@ -1,115 +1,26 @@
 import os
-import gradio as gr
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from huggingface_hub import login
-
-# --- Basic Setup ---
-HF_READONLY_API_KEY = os.getenv("HF_READONLY_API_KEY")
-if HF_READONLY_API_KEY:
-    login(token=HF_READONLY_API_KEY)
-
-SYSTEM_PROMPT = """You are a guardian model evaluating…</explanation>"""
-MODEL_NAME = "Qwen/Qwen3-0.6B"
-
-# --- LAZY LOADING SETUP ---
-# We initialize the model and tokenizer as None. They will be loaded on the first call.
-model = None
-tokenizer = None
-
-def load_model_and_tokenizer():
-    """
-    Loads the model and tokenizer if they haven't been loaded yet.
-    This function will only run its main logic once.
-    """
-    global model, tokenizer
-    if model is None or tokenizer is None:
-        print("--- LAZY LOADING: Loading model and tokenizer for the first time... ---")
-        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-        tokenizer.pad_token_id = tokenizer.pad_token_id or tokenizer.eos_token_id
-        
-        model = AutoModelForCausalLM.from_pretrained(
-            MODEL_NAME, 
-            device_map="auto", 
-            torch_dtype=torch.bfloat16
-        ).eval()
-        print("--- Model and tokenizer loaded successfully. ---")
 
-def format_rules(rules):
-    formatted_rules = "<rules>\n"
-    for i, rule in enumerate(rules):
-        formatted_rules += f"{i + 1}. {rule}\n"
-    formatted_rules += "</rules>\n"
-    return formatted_rules
+# Forcefully disable Server-Side Rendering
+os.environ["GRADIO_ENABLE_SSR"] = "0"
 
-def format_transcript(transcript):
-    formatted_transcript = f"<transcript>\n{transcript}\n</transcript>\n"
-    return formatted_transcript
-
-# --- The Main Gradio Function ---
-def compliance_check(rules_text, transcript_text, thinking):
-    """
-    The main inference function for the Gradio app.
-    It ensures the model is loaded before running inference.
-    """
-    try:
-        # STEP 1: Ensure the model is loaded. This will only do work on the first run.
-        load_model_and_tokenizer()
-
-        # STEP 2: Your original, robust input validation.
-        if not rules_text or not rules_text.strip():
-            return "Error: Please provide at least one rule."
-        if not transcript_text or not transcript_text.strip():
-            return "Error: Please provide a transcript to analyze."
-
-        # STEP 3: Format the input and generate a response.
-        rules = [r.strip() for r in rules_text.split("\n") if r.strip()]
-        inp = format_rules(rules) + format_transcript(transcript_text)
-        
-        message = [
-            {'role': 'system', 'content': SYSTEM_PROMPT},
-            {'role': 'user', 'content': inp}
-        ]
-        prompt = tokenizer.apply_chat_template(message, tokenize=False, add_generation_prompt=True)
-
-        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+import gradio as gr
 
-        with torch.no_grad():
-            output_content = model.generate(
-                **inputs,
-                max_new_tokens=256,
-                pad_token_id=tokenizer.pad_token_id,
-                do_sample=True,
-                temperature=0.6,
-                top_p=0.95,
-            )
-        
-        # Decode only the newly generated part of the response.
-        output_text = tokenizer.decode(output_content[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
-        
-        return output_text.strip()
+print("Gradio imported. Starting minimal test app.")
 
-    except Exception as e:
-        # A simple, safe error handler.
-        print(f"An error occurred: {str(e)}")
-        return "An error occurred during processing. The application might be under heavy load or encountered a problem. Please try again."
+def minimal_test_function(text_input):
+    """A simple function that cannot fail."""
+    print("Test function executed successfully.")
+    return f"The app is stable. You entered: '{text_input}'"
 
-# --- Build the Gradio Interface ---
-# We keep your well-designed interface configuration.
+# A minimal interface with no complex dependencies
 demo = gr.Interface(
-    fn=compliance_check,
-    inputs=[
-        gr.Textbox(lines=5, label="Rules (one per line)", max_lines=10, placeholder="Enter compliance rules, one per line..."),
-        gr.Textbox(lines=10, label="Transcript", max_lines=15, placeholder="Paste the transcript to analyze..."),
-        gr.Checkbox(label="Enable ⟨think⟩ mode", value=True)
-    ],
-    outputs=gr.Textbox(label="Compliance Output", lines=10, max_lines=15, show_copy_button=True),
-    title="DynaGuard Compliance Checker",
-    description="Paste your rules & transcript, then hit Submit. The model will load on the first request, which may take a moment.",
-    allow_flagging="never",
-    cache_examples=False
+    fn=minimal_test_function,
+    inputs=gr.Textbox(label="Input Text"),
+    outputs=gr.Textbox(label="Output"),
+    title="Environment Stability Test",
+    description="If you see this page and can use it without it crashing, the environment is now fixed."
 )
 
-# --- Launch the App ---
 if __name__ == "__main__":
+    # Standard launch command
     demo.launch()