Change the app.py name

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSequenceClassification
+from peft import PeftModel
+import torch
+
+# Load base model, LoRA adapter and reward model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-1.8B")
+tokenizer_sft = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-1.8B")
+sft_model = PeftModel.from_pretrained(base_model, "Miao025/Qwen-KinderChatbot-LoRA").to(device)
+tokenizer_reward = AutoTokenizer.from_pretrained("Miao025/Qwen-KinderChatbot-Reward")
+reward_model = AutoModelForSequenceClassification.from_pretrained("Miao025/Qwen-KinderChatbot-Reward").to(device)
+
+# Generate a list of multiple (default to 5) responses using the fine-tuned model
+def generate_responses(prompt, n=5):
+    inputs = tokenizer_sft(prompt, return_tensors="pt", truncation=True).to(device) # "pt" means pytorch tensors so that the model can read
+    outputs = []
+    for i in range(n):
+        generated_ids = sft_model.generate(
+            **inputs, # the tokenized prompt
+            max_length=256, # the max total length of generated text
+            do_sample=True, # choose randomly instead of best next token to generate different answers
+            top_p=0.9, # keep the smallest set of tokens whose cumulative probability adds up to ≥ 0.9 to avoid nonsense
+            temperature=0.8 # control how sharp or flat the probability distribution is, the lower the less randomness
+            )
+        out = tokenizer_sft.decode(generated_ids[0], skip_special_tokens=True) # decode to human language, note to skip special tokens like padding
+        if out.lower().startswith(prompt.lower()): # remove the prompt from the beginning of the answer if present
+            out = out[len(prompt)+1:]
+        
+        outputs.append(out)
+    return outputs
+
+# Score each response using reward model
+def score_response(prompt, response):
+    inputs = tokenizer_reward(prompt, response, return_tensors="pt", truncation=True).to(device)
+    with torch.no_grad():
+        logits = reward_model(**inputs).logits # raw score before softmax
+        score = torch.softmax(logits, dim=-1)[0,1].item()  # apply softmax to get the possibility of chosen and rejected, then get the chosen with label=1, then convert it into float
+    return score
+
+# Choose the best response
+def return_best_response(prompt):
+    candidates = generate_responses(prompt, n=5)
+    scores = [(candidate, score_response(prompt, candidate)) for candidate in candidates]
+    best_response = max(scores, key=lambda x: x[1])[0]
+    return best_response
+
+# Gradio deploy
+import gradio as gr
+iface = gr.Interface(
+    fn=return_best_response,
+    inputs=gr.Textbox(lines=3, label="My sweetie, what is your question?:"),
+    outputs=gr.Textbox(label="AI teacher answers you:"),
+    title="SFT + Reward Reranker chatbot Demo"
+)
+
+iface.launch()