Update app.py

app.py

@@ -4,7 +4,7 @@ import gradio as gr
 from nltk.tokenize import sent_tokenize
 import torch
 
-model = "janny127/autotrain-7qmts-cs1er"
+model = "janny127/autotrain-pje3d-uvelc1"
 tokenizer = AutoTokenizer.from_pretrained(model)
 
 pipeline = pipeline(
@@ -35,6 +35,8 @@ def predict(prompt, history):
     final_result = generated_text.split("### Assistant:")[1]
     if " Human: " in final_result:
         final_result = final_result.split(" Human: ")[0]
+    if " #" in final_result:
+        final_result = final_result.split(" #")[0]
     
     # return generated_text.strip()
     return final_result.strip()
@@ -45,40 +47,4 @@ gr.ChatInterface(predict,
                  examples=['How to cook a fish?', 'Who is the president of US now?']
                  ).launch()  # Launching the web interface.
 
-# interface = gr.ChatInterface(
-#     fn=predict,
-#     stop_btn=None
-# )
-
-# with gr.Blocks() as demo:
-#     interface.render()
-
-
-# demo.launch()
-
-# def generate_answer(query, sample_num=3):
-#     formatted_prompt = (
-#         f"<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"
-
-#     )
-
-#     sequences = pipeline(
-#         formatted_prompt,
-#         do_sample=True,
-#         top_k=50,
-#         top_p = 0.9,
-#         num_return_sequences=sample_num,
-#         repetition_penalty=1.1,
-#         max_new_tokens=150,
-#         eos_token_id=CHAT_EOS_TOKEN_ID,
-#     )
-#     answers = list()
-#     for seq in sequences:
-#         answer = seq['generated_text'].replace(formatted_prompt, "")
-#         answers.append(answer)
-#     answer = sent_tokenize(answers[0])
-#     final_answer = ''
-#     for an in answer:
-#         final_answer += an.strip()
-#     return final_answer