Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+import torch
+#from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+#import pickle
+
+
+title = "🤖Deployment GUVI GPT Model using Hugging Face"
+description = "Building open-domain chatbots is a challenging area for machine learning research."
+examples = [["Guvi Details"]]
+
+model_name = "fine_tuned_model123"
+#model = GPT2LMHeadModel.from_pretrained(model_name)
+#tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+
+# Load the tokenizer and model from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+
+
+
+def predict(input, history=[]):
+    # tokenize the new input sentence
+    new_user_input_ids = tokenizer.encode(
+        input + tokenizer.eos_token, return_tensors="pt"
+    )
+
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+
+    # generate a response
+    history = model.generate(
+        bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
+    ).tolist()
+
+    # convert the tokens to text, and then split the responses into lines
+    response = tokenizer.decode(history[0]).split("<|endoftext|>")
+    # print('decoded_response-->>'+str(response))
+    response = [
+        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
+    ]  # convert to tuples of list
+    # print('response-->>'+str(response))
+    return response, history
+
+
+gr.Interface(
+    fn=predict,
+    title=title,
+    description=description,
+    examples=examples,
+    inputs=["text", "state"],
+    outputs=["chatbot", "state"],
+    theme="finlaymacklon/boxy_violet",
+).launch()