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chat.py

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+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+from transformers import pipeline
+import torch
+import os
+
+# Automatically get the current folder
+base_dir = os.path.dirname(os.path.abspath(__file__))
+model_path = os.path.join(base_dir, "chatbot")
+# path to the folder where all your files are
+tokenizer = GPT2Tokenizer.from_pretrained(model_path)
+model = GPT2LMHeadModel.from_pretrained(model_path)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# Create the text generation pipeline
+generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device=0 if torch.cuda.is_available() else -1
+)
+
+# Chat function
+def chat(temp=0.5):
+    print(f"\n🤖 Chatbot is ready! (temperature={temp}) — type 'exit' to quit.")
+    context = ""
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() == "exit":
+            break
+        context += f"A: {user_input}\nB:"
+        result = generator(
+            context,
+            max_length=len(tokenizer.encode(context)) + 50,
+            pad_token_id=tokenizer.eos_token_id,
+            do_sample=True,
+            top_k=50,
+            top_p=0.95,
+            temperature=temp
+        )[0]["generated_text"]
+        reply = result[len(context):].split("\n")[0].strip()
+        print(f"Bot: {reply}")
+        context += f"{reply}\n"
+
+# Start chatting
+if __name__ == "__main__":
+    chat(temp=0.8)

requirements.txt

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+transformers
+torch