Update README.md

README.md

@@ -1,38 +1,22 @@
-```
-Copied
-import json
-import requests
-headers = {"Authorization": f"Bearer {API_TOKEN}"}
-API_URL = "https://api-inference.huggingface.co/models/sillon/DialoGPT-small-HospitalBot"
-def query(payload):
-    data = json.dumps(payload)
-    response = requests.request("POST", API_URL, headers=headers, data=data)
-    return json.loads(response.content.decode("utf-8"))
-data = query(
-    {
-        "inputs": {
-            "past_user_inputs": ["Which movie is the best ?"],
-            "generated_responses": ["It's Die Hard for sure."],
-            "text": "Can you explain why ?",
-        },
-    }
-)
-# Response
-self.assertEqual(
-    data,
-    {
-        "generated_text": "It's the best movie ever.",
-        "conversation": {
-            "past_user_inputs": [
-                "Which movie is the best ?",
-                "Can you explain why ?",
-            ],
-            "generated_responses": [
-                "It's Die Hard for sure.",
-                "It's the best movie ever.",
-            ],
-        },
-        "warnings": ["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation."],
-    },
-)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+tokenizer = AutoTokenizer.from_pretrained("sillon/DialoGPT-small-HospitalBot")
+model = AutoModelForCausalLM.from_pretrained("sillon/DialoGPT-small-HospitalBot")
+
+# Let's chat for 5 lines
+for step in range(5):
+    # encode the new user input, add the eos_token and return a tensor in Pytorch
+    new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
+
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
+
+    # generated a response while limiting the total chat history to 1000 tokens, 
+    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+
+    # pretty print last ouput tokens from bot
+    print("HospitalBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
 ```