Update model_utils.py

model_utils.py

@@ -1,93 +1,98 @@
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
+import gradio as gr
 
-# Predetermined answers for common questions
+# All predetermined answers (now inside app.py for simplicity)
 PREDETERMINED_ANSWERS = {
-    "what is negative reinforcement": """
+    "negative reinforcement": """
 Negative reinforcement in ABA refers to the process where a behavior is strengthened by removing an aversive stimulus immediately after the behavior occurs. 
 
-Example: A child whines in the car until the parent turns off loud music. If the whining increases in the future when loud music plays, the whining was negatively reinforced by removal of the aversive sound.
+Example: A child whines in the car until the parent turns off loud music. If whining increases in the future when music plays, the behavior was negatively reinforced.
 
 Key points:
 - Removes something unpleasant
-- Strengthens the behavior that caused the removal
-- Different from punishment which decreases behavior
+- Strengthens the behavior
+- Different from punishment
 """,
     
-    "what is positive reinforcement": """
-Positive reinforcement occurs when a behavior is followed by the addition of a rewarding stimulus, increasing the likelihood of that behavior occurring again.
+    "positive reinforcement": """
+Positive reinforcement occurs when a behavior is followed by a rewarding stimulus, increasing the likelihood of that behavior recurring.
 
-Example: A child says "please" and receives a sticker. If saying "please" increases, the behavior was positively reinforced.
+Example: A child says "please" and receives praise. If "please" is used more often, the behavior was positively reinforced.
 
 Key characteristics:
 - Adds something desirable
-- Must be contingent on the behavior
+- Must be contingent on behavior
 - Most effective when immediate
-- Should be individualized to the learner
 """,
     
-    "what is aba": """
-Applied Behavior Analysis (ABA) is a scientific approach to understanding behavior and how it is affected by the environment. ABA focuses on:
+    "aba": """
+Applied Behavior Analysis (ABA) is a scientific approach to understanding behavior. Key aspects:
 
-1. Applying learning principles to increase useful behaviors
-2. Reducing behaviors that may interfere with learning
-3. Measuring behavior change objectively
+1. Increases useful behaviors
+2. Reduces interfering behaviors
+3. Uses data-driven decisions
+4. Individualized interventions
 
-Key components:
-- Data-driven decision making
-- Individualized interventions
-- Focus on socially significant behaviors
-- Based on decades of empirical research
+Considered gold-standard for autism treatment.
+""",
+    
+    "differential reinforcement": """
+Differential reinforcement reinforces specific behaviors while withholding reinforcement for others. Types include:
 
-ABA is considered the gold standard treatment for autism spectrum disorder.
+- DRA: Reinforcing alternative behaviors
+- DRO: Reinforcing when problem behavior is absent
+- DRI: Reinforcing incompatible behaviors
 """
 }
 
-PROMPT_TEMPLATE = """As a BCBA expert, provide a detailed response:
-Input: {question}
-Guidelines:
-1. Use ABA terminology correctly
-2. Cite evidence where possible
-3. Suggest practical interventions
-Response:"""
+def get_answer(question):
+    """Check for predetermined answers first, with flexible matching"""
+    question_lower = question.lower().strip()
+    
+    # Check if any predefined question is contained in the user's question
+    for key in PREDETERMINED_ANSWERS:
+        if key in question_lower:
+            return PREDETERMINED_ANSWERS[key]
+    
+    # Fallback for unknown questions
+    return "I can answer questions about: " + ", ".join([k for k in PREDETERMINED_ANSWERS.keys()])
 
-def load_model():
-    try:
-        model_name = "google/flan-t5-large"
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
-        model = AutoModelForSeq2SeqLM.from_pretrained(
-            model_name,
-            device_map="auto",
-            torch_dtype=torch.float16
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    # ABA Therapy Assistant
+    Get instant answers about Applied Behavior Analysis
+    """)
+    
+    with gr.Row():
+        question = gr.Textbox(
+            label="Ask about ABA",
+            placeholder="e.g. What is negative reinforcement?",
+            lines=2
         )
-        return tokenizer, model
-    except Exception as e:
-        print(f"Model loading failed: {e}")
-        return None, None
-
-def generate_response(question, tokenizer, model):
-    # Check for predetermined answers first
-    lower_question = question.strip().lower()
-    if lower_question in PREDETERMINED_ANSWERS:
-        return PREDETERMINED_ANSWERS[lower_question]
+        submit_btn = gr.Button("Get Answer", variant="primary")
     
-    # If model is available, use it
-    if tokenizer and model:
-        try:
-            prompt = PROMPT_TEMPLATE.format(question=question)
-            inputs = tokenizer(prompt, return_tensors="pt", truncation=True).to(model.device)
-            
-            outputs = model.generate(
-                **inputs,
-                max_new_tokens=250,
-                temperature=0.7,
-                num_beams=3,
-                top_p=0.9,
-                early_stopping=True
-            )
-            return tokenizer.decode(outputs[0], skip_special_tokens=True)
-        except Exception as e:
-            print(f"Model generation failed: {e}")
+    answer = gr.Textbox(
+        label="Answer",
+        interactive=False,
+        lines=6,
+        elem_classes=["answer-box"]
+    )
     
-    # Fallback response if model fails
-    return "I'm unable to generate a response right now. Here are some common ABA terms I can explain:\n- What is ABA?\n- What is negative reinforcement?\n- What is positive reinforcement?\n\nPlease ask about one of these or try again later."
+    # Add examples (but not as clickable suggestions)
+    gr.Markdown("### Common Questions:")
+    gr.Examples(
+        examples=[
+            "What is negative reinforcement?",
+            "Explain positive reinforcement",
+            "Tell me about ABA",
+            "What's differential reinforcement?"
+        ],
+        inputs=question,
+        label="Try asking:",
+        fn=get_answer,
+        outputs=answer
+    )
+    
+    submit_btn.click(get_answer, inputs=question, outputs=answer)
+
+demo.launch()