Create app.py

app.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+# ✅ 1. Load GPT-Neo model
+model_name = "EleutherAI/gpt-neo-1.3B"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+
+# ✅ 2. Chatbot-style Text Generator
+def chat_with_gptneo(history, user_input):
+    # Combine chat history for context (optional, keep recent exchanges)
+    if history:
+        past = "\n".join([f"User: {msg[0]}\nBot: {msg[1]}" for msg in history])
+        prompt = f"{past}\nUser: {user_input}\nBot:"
+    else:
+        prompt = f"User: {user_input}\nBot:"
+
+    inputs = tokenizer(prompt, return_tensors='pt').to(device)
+
+    outputs = model.generate(
+        inputs['input_ids'],
+        max_new_tokens=200,
+        do_sample=True,
+        temperature=0.85,
+        top_k=50,
+        top_p=0.95,
+        repetition_penalty=1.2,
+        no_repeat_ngram_size=3,
+        pad_token_id=tokenizer.eos_token_id,
+        early_stopping=True
+    )
+
+    full_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response = full_output.replace(prompt, "").strip()
+    return response
+
+# ✅ 3. Gradio Chat Interface
+with gr.Blocks(title="GPT-Neo Chatbot") as demo:
+    gr.Markdown("## 🤖 GPT-Neo 1.3B Chatbot\nAsk me anything!")
+    
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(placeholder="Type your prompt here...", label="Your Prompt")
+    clear = gr.Button("Clear")
+
+    history = gr.State([])
+
+    def respond(user_message, chat_history):
+        bot_message = chat_with_gptneo(chat_history, user_message)
+        chat_history.append((user_message, bot_message))
+        return "", chat_history
+
+    msg.submit(respond, [msg, history], [msg, chatbot])
+    clear.click(lambda: ([], []), None, [history, chatbot])
+
+# ✅ 4. Launch the app
+demo.launch()