Create app.py

app.py

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+import streamlit as st
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+from peft import get_peft_model, LoraConfig
+
+# Define the same LoRA configuration used during fine-tuning
+lora_config = LoraConfig(
+    r=8,  # Low-rank parameter
+    lora_alpha=32,  # Scaling parameter
+    lora_dropout=0.1,  # Dropout rate
+    target_modules=["q", "v"],  # The attention layers to apply LoRA to
+    bias="none"
+)
+
+# Load the model and tokenizer from Hugging Face's hub
+model = get_peft_model(T5ForConditionalGeneration.from_pretrained("google/flan-t5-large"), lora_config)
+tokenizer = T5Tokenizer.from_pretrained("danrdoran/flan-t5-simplified-squad")
+
+# Streamlit app UI
+st.title("AI English Tutor")
+st.write("Ask me a question, and I will help you!")
+
+# Sidebar for user to control model generation parameters
+st.sidebar.title("Model Parameters")
+temperature = st.sidebar.slider("Temperature", 0.1, 1.5, 1.0, 0.1)  # Default 1.0
+top_p = st.sidebar.slider("Top-p (Nucleus Sampling)", 0.0, 1.0, 0.9, 0.05)  # Default 0.9
+top_k = st.sidebar.slider("Top-k", 0, 100, 50, 1)  # Default 50
+# Disable sampling when using beam search
+do_sample = st.sidebar.checkbox("Enable Random Sampling", value=False)
+
+# Input field for the student
+student_question = st.text_input("Ask your question!")
+
+# Generate and display response using the model's generate() function
+if student_question:
+    # Prepare the input for the model
+    input_text = f"You are a tutor. Explain the answer to this question to a young student: '{student_question}'"
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=256)  # Reduced max_length to 256
+
+    # Generate response
+    generated_ids = model.generate(
+        inputs['input_ids'],
+        #max_length=75,
+        #min_length=20,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        do_sample=True,  # Disable sampling, using beam search
+        #num_beams=2,  # Use beam search
+        no_repeat_ngram_size=3,  # Prevent repeating phrases of 3 words or more
+        length_penalty=1.0,  # Discourage overly long responses
+        early_stopping=False  # Stops when it finds a sufficiently good output
+    )
+
+    # Decode the generated response
+    response = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+    st.write("Tutor's Answer:", response)