better install shell based on issue comment

run_web_demo.sh

@@ -3,43 +3,73 @@
 cd "$(dirname "$0")"
 thisDir=$(pwd)
 
+export INSTALL_DEPS=false
+export INSTALL_FLASH_ATTN=false
+
+declare -a PASS_THROUGH_ARGS=()
+
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+    -h | --help)
+        echo "Usage: $0 [-h|--help] [--install-deps] [--install-flash-attn]"
+        exit 0
+        ;;
+    --install-deps)
+        export INSTALL_DEPS=true
+        shift
+        ;;
+    --install-flash-attn)
+        export INSTALL_FLASH_ATTN=true
+        shift
+        ;;
+    -)
+        shift
+        PASS_THROUGH_ARGS=($@)
+        break
+        ;;
+
+    *)
+        echo "Unknown option: $1"
+        exit 1
+        ;;
+    esac
+done
+
+echo "INSTALL_DEPS: $INSTALL_DEPS"
+echo "INSTALL_FLASH_ATTN: $INSTALL_FLASH_ATTN"
+echo "PASS_THROUGH_ARGS: ${PASS_THROUGH_ARGS[@]}"
+
 function performInstall() {
-    set -e
 
     pushd "$thisDir"
     pip3 install -r requirements.txt
-    pip3 install gradio mdtex2html scipy
+    pip3 install gradio mdtex2html scipy argparse
 
-    if [[ ! -d flash-attention ]]; then
-        if ! git clone -b v1.0.8 https://github.com/Dao-AILab/flash-attention; then
-            echo "Clone flash-attention failed, please install it manually."
-            return 0
+    if $INSTALL_FLASH_ATTN; then
+        if [[ ! -d flash-attention ]]; then
+            if ! git clone -b v1.0.8 https://github.com/Dao-AILab/flash-attention; then
+                echo "Clone flash-attention failed, please install it manually."
+                return 0
+            fi
         fi
+
+        cd flash-attention &&
+            pip3 install . &&
+            pip3 install csrc/layer_norm &&
+            pip3 install csrc/rotary ||
+            echo "Install flash-attention failed, please install it manually."
     fi
 
-    cd flash-attention &&
-        pip3 install . &&
-        pip3 install csrc/layer_norm &&
-        pip3 install csrc/rotary ||
-        echo "Install flash-attention failed, please install it manually."
     popd
 }
 
 echo "Starting WebUI..."
 
-if ! python3 web_demo.py; then
-    echo "Run demo failed, install the deps and try again? (y/n)"
-    # auto perform install if in docker
-    if [[ -t 0 ]] && [[ -t 1 ]] && [[ ! -f "/.dockerenv" ]]; then
-        read doInstall
+if ! python3 web_demo.py ${PASS_THROUGH_ARGS[@]}; then
+    if $INSTALL_DEPS; then
+        echo "Installing deps, and try again..."
+        performInstall && python3 web_demo.py ${PASS_THROUGH_ARGS[@]}
     else
-        doInstall="y"
+        echo "Please install deps manually, or use --install-deps to install deps automatically."
     fi
-
-    if ! [[ "$doInstall" =~ y|Y ]]; then
-        exit 1
-    fi
-
-    echo "Installing deps, and try again..."
-    performInstall && python3 web_demo.py
 fi

web_demo.py

@@ -7,21 +7,14 @@ import gradio as gr
 import mdtex2html
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from transformers.generation import GenerationConfig
+from argparse import ArgumentParser
 import sys
 
-tokenizer = AutoTokenizer.from_pretrained(
-    "Qwen/Qwen-7B-Chat", trust_remote_code=True, resume_download=True
-)
-model = AutoModelForCausalLM.from_pretrained(
-    "Qwen/Qwen-7B-Chat",
-    device_map="auto",
-    offload_folder="offload",
-    trust_remote_code=True,
-    resume_download=True,
-).eval()
-model.generation_config = GenerationConfig.from_pretrained(
-    "Qwen/Qwen-7B-Chat", trust_remote_code=True, resume_download=True
-)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True, resume_download=True)
+
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", offload_folder="offload", trust_remote_code=True, resume_download=True).eval()
+
+model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True, resume_download=True)
 
 if len(sys.argv) > 1 and sys.argv[1] == "--exit":
     sys.exit(0)
@@ -82,7 +75,7 @@ def predict(input, chatbot):
     chatbot.append((parse_text(input), ""))
     fullResponse = ""
 
-    for response in model.chat(tokenizer, input, history=task_history, stream=True):
+    for response in model.chat_stream(tokenizer, input, history=task_history):
         chatbot[-1] = (parse_text(input), parse_text(response))
 
         yield chatbot
@@ -108,9 +101,7 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column(scale=4):
             with gr.Column(scale=12):
-                query = gr.Textbox(
-                    show_label=False, placeholder="Input...", lines=10
-                ).style(container=False)
+                query = gr.Textbox(show_label=False, placeholder="Input...", lines=10).style(container=False)
             with gr.Column(min_width=32, scale=1):
                 submitBtn = gr.Button("Submit", variant="primary")
         with gr.Column(scale=1):
@@ -120,4 +111,18 @@ with gr.Blocks() as demo:
     submitBtn.click(reset_user_input, [], [query])
     emptyBtn.click(reset_state, outputs=[chatbot], show_progress=True)
 
-demo.queue().launch(share=False, inbrowser=True, server_port=80, server_name="0.0.0.0")
+if len(sys.argv) > 1:
+
+    print("Call args:" + str(sys.argv))
+    parser = ArgumentParser()
+    parser.add_argument("--share", action="store_true", default=False)
+    parser.add_argument("--inbrowser", action="store_true", default=False)
+    parser.add_argument("--server_port", type=int, default=80)
+    parser.add_argument("--server_name", type=str, default="0.0.0.0")
+    args = parser.parse_args(sys.argv[1:])
+    print("Args:" + str(args))
+
+    print("Args:" + str(args))
+    demo.queue().launch(args)
+else:
+    demo.queue().launch()