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app.py

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+# -*- coding:utf-8 -*-
+import os
+import logging
+import sys
+import gradio as gr
+import torch
+from app_modules.utils import *
+from app_modules.presets import *
+from app_modules.overwrites import *
+
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
+)
+
+base_model = "decapoda-research/llama-7b-hf"
+adapter_model = "project-baize/baize-lora-7B"
+#tokenizer,model,device = load_tokenizer_and_model(base_model,adapter_model)
+
+
+def predict(text,
+            chatbot,
+            history,
+            top_p,
+            temperature,
+            max_length_tokens,
+            max_context_length_tokens,):
+    if text=="":
+        return history,history,"Empty Context"
+
+    inputs = generate_prompt_with_history(text,history,tokenizer,max_length=max_context_length_tokens)
+    if inputs is False:
+        return [[x[0],convert_to_markdown(x[1])] for x in history]+[[text,"Sorry, the input is too long."]],history,"Generate Fail"
+    else:
+        prompt,inputs=inputs
+        begin_length = len(prompt)
+    
+    input_ids = inputs["input_ids"].to(device)
+
+    with torch.no_grad():
+        for x in greedy_search(input_ids,model,tokenizer,stop_words=["[|Human|]", "[|AI|]"],max_length=max_length_tokens,temperature=temperature,top_p=top_p):
+            if is_stop_word_or_prefix(x,["[|Human|]", "[|AI|]"]) is False:
+                if "[|Human|]" in x:
+                    x = x[:x.index("[|Human|]")].strip()
+                if "[|AI|]" in x:
+                    x = x[:x.index("[|AI|]")].strip() 
+                x = x.strip(" ")   
+                a, b=   [[y[0],convert_to_markdown(y[1])] for y in history]+[[text, convert_to_markdown(x)]],history + [[text,x]]
+                yield a, b, "Generating……"
+            if shared_state.interrupted:
+                shared_state.recover()
+                try:
+                    yield a, b, "Stop Success"
+                    return
+                except:
+                    pass
+    print(prompt)
+    print(x)
+    print("="*80)
+    try:
+        yield a,b,"Generate Success"
+    except:
+        pass
+        
+def retry(
+        text,
+        chatbot,
+        history,
+        top_p,
+        temperature,
+        max_length_tokens,
+        max_context_length_tokens,
+        ):
+    logging.info("Retry……")
+    if len(history) == 0:
+        yield chatbot, history, f"Empty context"
+        return
+    chatbot.pop()
+    inputs = history.pop()[0]
+    for x in predict(inputs,chatbot,history,top_p,temperature,max_length_tokens,max_context_length_tokens):
+        yield x
+
+
+gr.Chatbot.postprocess = postprocess
+
+with open("assets/custom.css", "r", encoding="utf-8") as f:
+    customCSS = f.read()
+
+with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
+    history = gr.State([])
+    user_question = gr.State("")
+    with gr.Row():
+        gr.HTML(title)
+        status_display = gr.Markdown("Success", elem_id="status_display")
+    gr.Markdown(description_top)
+    with gr.Row(scale=1).style(equal_height=True):
+        with gr.Column(scale=5):
+            with gr.Row(scale=1):
+                chatbot = gr.Chatbot(elem_id="chuanhu_chatbot").style(height="80%")
+            with gr.Row(scale=1):
+                with gr.Column(scale=12):
+                    user_input = gr.Textbox(
+                        show_label=False, placeholder="Enter text"
+                    ).style(container=False)
+                with gr.Column(min_width=70, scale=1):
+                    submitBtn = gr.Button("Send")
+                with gr.Column(min_width=70, scale=1):  
+                    cancelBtn = gr.Button("Stop")
+                  
+            with gr.Row(scale=1):
+                emptyBtn = gr.Button(
+                    "🧹 New Conversation",
+                )
+                retryBtn = gr.Button("🔄 Regenerate")
+                delLastBtn = gr.Button("🗑️ Remove Last Turn") 
+        with gr.Column():
+            with gr.Column(min_width=50, scale=1):
+                with gr.Tab(label="Parameter Setting"):
+                    gr.Markdown("# Parameters")
+                    top_p = gr.Slider(
+                        minimum=-0,
+                        maximum=1.0,
+                        value=0.95,
+                        step=0.05,
+                        interactive=True,
+                        label="Top-p",
+                    )
+                    temperature = gr.Slider(
+                        minimum=-0,
+                        maximum=2.0,
+                        value=1,
+                        step=0.1,
+                        interactive=True,
+                        label="Temperature",
+                    )
+                    max_length_tokens = gr.Slider(
+                        minimum=0,
+                        maximum=512,
+                        value=512,
+                        step=8,
+                        interactive=True,
+                        label="Max Generation Tokens",
+                    )
+                    max_context_length_tokens = gr.Slider(
+                        minimum=0,
+                        maximum=4096,
+                        value=2048,
+                        step=128,
+                        interactive=True,
+                        label="Max History Tokens",
+                    )
+    gr.Markdown(description)
+
+    predict_args = dict(
+        fn=predict,
+        inputs=[
+            user_question,
+            chatbot,
+            history,
+            top_p,
+            temperature,
+            max_length_tokens,
+            max_context_length_tokens,
+        ],
+        outputs=[chatbot, history, status_display],
+        show_progress=True,
+    )
+    retry_args = dict(
+        fn=retry,
+        inputs=[
+            user_input,
+            chatbot,
+            history,
+            top_p,
+            temperature,
+            max_length_tokens,
+            max_context_length_tokens,
+        ],
+        outputs=[chatbot, history, status_display],
+        show_progress=True,
+    )
+
+    reset_args = dict(
+        fn=reset_textbox, inputs=[], outputs=[user_input, status_display]
+    )
+ 
+    # Chatbot
+    cancelBtn.click(cancel_outputing, [], [ status_display])
+    transfer_input_args = dict(
+        fn=transfer_input, inputs=[user_input], outputs=[user_question, user_input, submitBtn, cancelBtn], show_progress=True
+    )
+
+    user_input.submit(**transfer_input_args).then(**predict_args)
+
+    submitBtn.click(**transfer_input_args).then(**predict_args)
+
+    emptyBtn.click(
+        reset_state,
+        outputs=[chatbot, history, status_display],
+        show_progress=True,
+    )
+    emptyBtn.click(**reset_args)
+
+    retryBtn.click(**retry_args)
+
+    delLastBtn.click(
+        delete_last_conversation,
+        [chatbot, history],
+        [chatbot, history, status_display],
+        show_progress=True,
+    )
+
+demo.title = "Baize"
+
+if __name__ == "__main__":
+    reload_javascript()
+    demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
+        share=True, favicon_path="./assets/favicon.ico", inbrowser=True
+    )

requirements.txt

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+gradio
+mdtex2html
+pypinyin
+tiktoken
+socksio
+tqdm
+colorama
+duckduckgo_search
+Pygments
+llama_index
+langchain
+markdown
+markdown2
+