Create app.py

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+import torch
+
+
+title = "🦅Falcon 🗨️ChatBot"
+description ="Falcon-RW-1B is a 1B parameters causal decoder-only model built by TII and trained on 350B tokens of RefinedWeb."
+examples = [["How are you?"]]
+
+
+tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-rw-1b")
+model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-rw-1b")
+
+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()