Create app.py

app.py

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+from transformers import GPTNeoForCausalLM, GPT2Tokenizer
+import gradio as gr
+model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M")
+tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-125M")
+prompt = """This is a discussion between a person and an entrepreneur. 
+person: What is your name?
+entrepreneur: Mr. Patel
+person: Where are you working?
+entrepreneur: It's like one of these fancy adjustable height desk
+person: What will you work on?
+entrepreneur: The international development hackathon
+person: What are you working on?
+entrepreneur: Developping an classification web app
+person: """
+def my_split(s, seps):
+    res = [s]
+    for sep in seps:
+        s, res = res, []
+        for seq in s:
+            res += seq.split(sep)
+    return res
+# input = "Who are you?"
+def chat_base(input):
+  p = prompt + input
+  input_ids = tokenizer(p, return_tensors="pt").input_ids
+  gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.7, max_length=150,)
+  gen_text = tokenizer.batch_decode(gen_tokens)[0]
+  # print(gen_text)
+  result = gen_text[len(p):]   
+  # print(">", result)
+  result = my_split(result, [']', '\n'])[1]
+  # print(">>", result)
+  result = result[14:]
+# print(">>>", result)
+  return result
+  
+  import gradio as gr
+def chat(message):
+    history = gr.get_state() or []
+    print(history)
+    response = chat_base(message)
+    history.append((message, response))
+    gr.set_state(history)
+    html = "<div class='chatbot'>"
+    for user_msg, resp_msg in history:
+        html += f"<div class='user_msg'>{user_msg}</div>"
+        html += f"<div class='resp_msg'>{resp_msg}</div>"
+    html += "</div>"
+    return html
+iface = gr.Interface(chat, gr.inputs.Textbox(label="Ask Hassan a Question"), "html", css="""
+    .chatbox {display:flex;flex-direction:column}
+    .user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
+    .user_msg {background-color:cornflowerblue;color:white;align-self:start}
+    .resp_msg {background-color:lightgray;align-self:self-end}
+""", allow_screenshot=False, allow_flagging=False)
+iface.launch()