Update feedback.py

feedback.py

@@ -1,18 +1,33 @@
-from transformers import GPT3LMHeadModel, GPT3Tokenizer
+from flask import Flask, render_template, request
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-# Load pre-trained model and tokenizer
-model_name = "gpt3"  # You could choose another model here
-model = GPT2LMHeadModel.from_pretrained(model_name)
-tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+app = Flask(__name__)
 
-def generate_feedback(user_answer):
-    # Encode user input to model's input format
-    inputs = tokenizer.encode(user_answer, return_tensors='pt')
-    
-    # Generate response from the model
-    outputs = model.generate(inputs, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2)
+# Load the fine-tuned model and tokenizer
+model = GPT2LMHeadModel.from_pretrained("./fine_tuned_model")
+tokenizer = GPT2Tokenizer.from_pretrained("./fine_tuned_model")
 
-    # Decode the model's output to text
+# Function to generate feedback
+def generate_feedback(user_input):
+    inputs = tokenizer(user_input, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    outputs = model.generate(inputs["input_ids"], max_length=150, num_return_sequences=1)
     feedback = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
     return feedback
+
+@app.route('/')
+def index():
+    return render_template('behavioral.html')
+
+@app.route('/submit_answer', methods=['POST'])
+def submit_answer():
+    # Get the user input from the form
+    user_input = request.form['answer']  # Assuming the form uses 'answer' as input field name
+    
+    # Generate feedback using the fine-tuned model
+    feedback = generate_feedback(user_input)
+    
+    # Return the feedback to the user and render it in behavioral.html
+    return render_template('behavioral.html', feedback=feedback)
+
+if __name__ == '__main__':
+    app.run(debug=True)