Update app.py with Gradio interface

app.py

@@ -0,0 +1,52 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import random
+import gradio as gr
+
+# Replace with your actual model path
+transformers_model_path = "jingyaogong/MiniMind2"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(transformers_model_path)
+model = AutoModelForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True).eval()
+
+def setup_seed(seed):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    random.seed(seed)
+
+def predict(prompt):
+    messages = []
+    max_seq_len = 128
+    history_cnt = 0
+    model_mode = 2
+    setup_seed(random.randint(0, 2048))
+    messages = messages[-history_cnt:] if history_cnt else []
+    messages.append({"role": "user", "content": prompt})
+    new_prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )[-max_seq_len - 1:] if model_mode != 0 else (tokenizer.bos_token + prompt)
+
+    with torch.no_grad():
+        x = torch.tensor(tokenizer(new_prompt)['input_ids'], device='cpu').unsqueeze(0)
+        outputs = model.generate(
+            x,
+            eos_token_id=tokenizer.eos_token_id,
+            max_new_tokens=max_seq_len,
+            temperature=0.7,
+            top_p=0.95,
+            pad_token_id=tokenizer.pad_token_id
+        )
+        return tokenizer.decode(outputs.squeeze()[x.shape[1]:].tolist(), skip_special_tokens=True)
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your text here..."),
+    outputs="text",
+    title="MiniMind2 Chatbot",
+    description="Enter text and see the model's response."
+)
+
+iface.launch()

requirements.txt

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+torch
+transformers
+gradio