Update app.py

app.py

@@ -1,21 +1,4 @@
 #!/usr/bin/env python3
-#
-# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
-#
-# See LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
 # References:
 # https://gradio.app/docs/#dropdown
 
@@ -37,7 +20,7 @@ from model import decode, get_pretrained_model, language_to_models, sample_rate
 
 languages = list(language_to_models.keys())
 
-
+# 将输入的音频文件转换为.wav格式。它使用ffmpeg工具将输入文件转换为16kHz的.wav文件，然后对转换后的文件进行base64编码。
 def convert_to_wav(in_filename: str) -> str:
     """Convert the input audio file to a wave file"""
     out_filename = in_filename + ".wav"
@@ -54,7 +37,7 @@ def convert_to_wav(in_filename: str) -> str:
 
     return out_filename
 
-
+# 函数build_html_output用于构建HTML格式的输出结果。
 def build_html_output(s: str, style: str = "result_item_success"):
     return f"""
     <div class='result'>
@@ -64,57 +47,7 @@ def build_html_output(s: str, style: str = "result_item_success"):
     </div>
     """
 
-def process_url(
-    language: str,
-    repo_id: str,
-    decoding_method: str,
-    num_active_paths: int,
-    url: str,
-):
-    logging.info(f"Processing URL: {url}")
-    with tempfile.NamedTemporaryFile() as f:
-        try:
-            urllib.request.urlretrieve(url, f.name)
-
-            return process(
-                in_filename=f.name,
-                language=language,
-                repo_id=repo_id,
-                decoding_method=decoding_method,
-                num_active_paths=num_active_paths,
-            )
-        except Exception as e:
-            logging.info(str(e))
-            return "", build_html_output(str(e), "result_item_error")
-
-def process_uploaded_file(
-    language: str,
-    repo_id: str,
-    decoding_method: str,
-    num_active_paths: int,
-    in_filename: str,
-):
-    if in_filename is None or in_filename == "":
-        return "", build_html_output(
-            "Please first upload a file and then click "
-            'the button "submit for recognition"',
-            "result_item_error",
-        )
-
-    logging.info(f"Processing uploaded file: {in_filename}")
-    try:
-        return process(
-            in_filename=in_filename,
-            language=language,
-            repo_id=repo_id,
-            decoding_method=decoding_method,
-            num_active_paths=num_active_paths,
-        )
-    except Exception as e:
-        logging.info(str(e))
-        return "", build_html_output(str(e), "result_item_error")
-
-
+# 负责处理用户通过麦克风录制的音频文件。它会检查录音是否存在，然后调用process函数进行处理。如果处理过程中出现错误，它会捕获异常并返回错误信息。
 def process_microphone(
     language: str,
     repo_id: str,
@@ -143,7 +76,7 @@ def process_microphone(
         logging.info(str(e))
         return "", build_html_output(str(e), "result_item_error")
 
-
+# 它处理音频输入，执行语音识别，并返回识别的文本和一些元数据信息。它首先调用 convert_to_wav 将音频转换为.wav格式，然后获取当前时间并开始计时。之后，它调用 get_pretrained_model 来获取预训练模型，并使用 decode 函数对音频进行识别。最后，它计算音频的总时长，处理时间以及实时因子（RTF，即处理时间除以音频时长）。
 @torch.no_grad()
 def process(
     language: str,
@@ -200,26 +133,8 @@ def process(
     return text, build_html_output(info)
 
 
-title = "# Automatic Speech Recognition with Next-gen Kaldi"
-description = """
-This space shows how to do automatic speech recognition with Next-gen Kaldi.
-
-Please visit
-<https://huggingface.co/spaces/k2-fsa/streaming-automatic-speech-recognition>
-for streaming speech recognition with **Next-gen Kaldi**.
+title = "# For Interview!!! Fight!"
 
-It is running on CPU within a docker container provided by Hugging Face.
-
-See more information by visiting the following links:
-
-- <https://github.com/k2-fsa/icefall>
-- <https://github.com/k2-fsa/sherpa>
-- <https://github.com/k2-fsa/k2>
-- <https://github.com/lhotse-speech/lhotse>
-
-If you want to deploy it locally, please see
-<https://k2-fsa.github.io/sherpa/>
-"""
 
 # css style is copied from
 # https://huggingface.co/spaces/alphacep/asr/blob/main/app.py#L113
@@ -239,35 +154,135 @@ def update_model_dropdown(language: str):
     raise ValueError(f"Unsupported language: {language}")
 
 
-demo = gr.Blocks(css=css)
-
+import openai
+import gradio as gr
 
+# Load your API key from an environment variable or secret management service
+openai.api_key = "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"
+# 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
+
+
+####################################################################################################################
+##################################       Prompt1 - ask questions     ###############################################
+####################################################################################################################
+messages = [
+  {
+    "role": "system",
+    "content": "You are now serving as an HR for a technology company and would like to ask me some interview questions. If you find my answers difficult to understand or unclear, please feel free to ask me to clarify the unclear parts. Alternatively, if you are interested, you can also ask me some follow-up questions. Please do not attempt to correct my answers or provide suggestions. The conversation should simulate the real interview."
+  },
+]
+
+def process_input(user_message):
+    global messages
+
+    # Append user message to conversation
+    messages.append({"role": "user", "content": user_message})
+
+    # Call OpenAI API
+    response = openai.ChatCompletion.create(model="gpt-3.5-turbo-16k", messages=messages)
+
+    # Get the assistant's message from the response
+    assistant_message = response['choices'][0]['message']['content']
+
+    # Append assistant message to conversation
+    messages.append({"role": "assistant", "content": assistant_message})
+
+    # Create conversation history
+    conversation_history = ""
+    for message in messages:
+        role = message["role"]
+        content = message["content"]
+        conversation_history += f"{role.title()}: {content}\n"
+
+    return assistant_message, conversation_history
+
+def generate_download_content():
+    global messages
+
+    # Create conversation history
+    conversation_history = ""
+    for message in messages:
+        role = message["role"]
+        content = message["content"]
+        conversation_history += f"{role.title()}: {content}\n"
+
+    return conversation_history
+
+apple = "The quick brown fox jumps over the lazy cat."
+textbox_input = gr.inputs.Textbox(default=apple)
+tts_interface = gr.load("huggingface/facebook/fastspeech2-en-ljspeech",
+                inputs=textbox_input,
+                description="TTS using FastSpeech2",
+                title="Text to Speech (TTS)",
+                examples=[["The quick brown fox jumps over the lazy dog."]])
+
+
+####################################################################################################################
+##################################       Prompt2 - return result     ###############################################
+####################################################################################################################
+# results
+def langchain_query(txt_file, user_message):
+    # Open the file
+    if txt_file is None:
+        preloaded_text = "what do you think of the conversations below?..."
+    else:
+        preloaded_text = txt_file.read().decode('utf-8')
+
+    results = [  {
+    "role": "system",
+    "content": "You are now a professional job analysis analyst. Below, I need you to help me provide advice to the job seeker in the following conversation."
+  }, {"role": "user", "content": preloaded_text + "below is the conversation between me and HR, How do you think I performed? Based on our previous conversation, please correct my response to make it more logical, structured, professional and colloquial. My reply may contain some filler words and verbal tics(catchphrase); please provide suggestions for improvement in this area as well, with the aim of meeting Australian workplace standards. If my answer is too short(less than 2 minutes), you should give advices on how to expand my answer." + user_message}]
+    
+    # Call OpenAI API
+    response = openai.ChatCompletion.create(model="gpt-3.5-turbo-16k", messages=results)
+
+    # Get the assistant's message from the response
+    assistant_message = response['choices'][0]['message']['content']
+  
+    return assistant_message
+
+###############################################################################
+######################### 页面 ###################################
+################################################################
+# 创建了一个 gr.Blocks 对象，然后在其中添加了一些组件，如 gr.Markdown，gr.Radio，gr.Dropdown，gr.Slider，gr.Tabs，gr.TabItem，gr.Audio，gr.Button，gr.Textbox，gr.HTML 和 gr.Examples。所有这些组件都被配置为以特定方式响应用户输入。
+demo = gr.Blocks(css=css)
 with demo:
+    # 创建一个Markdown文本区域，用于显示标题。
     gr.Markdown(title)
-    language_choices = list(language_to_models.keys())
+    # 获取所有可用的语言选项。
+    # language_choices = list(language_to_models.keys())
+    language_choices = ['English']
+    print(language_choices)
 
+    # 接下来的一段代码创建了一个Radio按钮组和一个下拉菜单，用于选择语言和相应的模型。
     language_radio = gr.Radio(
         label="Language",
         choices=language_choices,
         value=language_choices[0],
     )
+    # print(f"radio: {language_radio}")
+    
     model_dropdown = gr.Dropdown(
         choices=language_to_models[language_choices[0]],
         label="Select a model",
         value=language_to_models[language_choices[0]][0],
     )
+    print(f"dropdown: {model_dropdown}")
 
+    # 当用户在Radio按钮组中更改选项时，会更新下拉菜单的内容。
     language_radio.change(
         update_model_dropdown,
         inputs=language_radio,
         outputs=model_dropdown,
     )
 
+    # 创建了另一个Radio按钮组和一个滑块，用户可以选择解码方法，并设置活动路径的数量。
     decoding_method_radio = gr.Radio(
         label="Decoding method",
         choices=["greedy_search", "modified_beam_search"],
         value="greedy_search",
     )
+    print(f"decoding_method_radio: {decoding_method_radio}")
 
     num_active_paths_slider = gr.Slider(
         minimum=1,
@@ -275,33 +290,33 @@ with demo:
         step=1,
         label="Number of active paths for modified_beam_search",
     )
-
-    with gr.Tabs():
-        with gr.TabItem("Upload from disk"):
-            uploaded_file = gr.Audio(
-                source="upload",  # Choose between "microphone", "upload"
-                type="filepath",
-                optional=False,
-                label="Upload from disk",
-            )
-            upload_button = gr.Button("Submit for recognition")
-            uploaded_output = gr.Textbox(label="Recognized speech from uploaded file")
-            uploaded_html_info = gr.HTML(label="Info")
-
-            gr.Examples(
-                examples=examples,
-                inputs=[
-                    language_radio,
-                    model_dropdown,
-                    decoding_method_radio,
-                    num_active_paths_slider,
-                    uploaded_file,
-                ],
-                outputs=[uploaded_output, uploaded_html_info],
-                fn=process_uploaded_file,
+    print(f"num_active_paths_slider: {num_active_paths_slider}")
+
+    
+    
+##############################################################################################
+#########################  主体部分   ##################################
+########################################################################
+    # 创建一个标签页容器。
+    
+    with gr.Tabs() as tabs:
+        
+        with gr.TabItem("Chat with AI"):
+            textbox_input = gr.inputs.Textbox(lines=5, placeholder="Type your message here...")
+            textbox_output = gr.outputs.Textbox(label="Assistant's response")
+            conversation_output = gr.outputs.Textbox(label="Conversation history")
+            submit_button = gr.Button("Submit")
+            # download_link = gr.outputs.Download(fn=generate_download_content, label="Download conversation")
+
+            submit_button.click(
+                process_input,
+                inputs=[textbox_input],
+                outputs=[textbox_output, conversation_output],
             )
-
+        
+        # 用户可以通过麦克风录制音频进行语音识别。
         with gr.TabItem("Record from microphone"):
+            gr.TabbedInterface([tts_interface], ["FastSpeech2"])
             microphone = gr.Audio(
                 source="microphone",  # Choose between "microphone", "upload"
                 type="filepath",
@@ -312,44 +327,8 @@ with demo:
             record_button = gr.Button("Submit for recognition")
             recorded_output = gr.Textbox(label="Recognized speech from recordings")
             recorded_html_info = gr.HTML(label="Info")
-
-            gr.Examples(
-                examples=examples,
-                inputs=[
-                    language_radio,
-                    model_dropdown,
-                    decoding_method_radio,
-                    num_active_paths_slider,
-                    microphone,
-                ],
-                outputs=[recorded_output, recorded_html_info],
-                fn=process_microphone,
-            )
-
-        with gr.TabItem("From URL"):
-            url_textbox = gr.Textbox(
-                    max_lines=1,
-                    placeholder="URL to an audio file",
-                    label="URL",
-                    interactive=True,
-            )
-
-            url_button = gr.Button("Submit for recognition")
-            url_output = gr.Textbox(label="Recognized speech from URL")
-            url_html_info = gr.HTML(label="Info")
-
-        upload_button.click(
-            process_uploaded_file,
-            inputs=[
-                language_radio,
-                model_dropdown,
-                decoding_method_radio,
-                num_active_paths_slider,
-                uploaded_file,
-            ],
-            outputs=[uploaded_output, uploaded_html_info],
-        )
-
+            
+        # 当用户点击录音按钮或URL按钮时，也会调用相应的处理函数。
         record_button.click(
             process_microphone,
             inputs=[
@@ -362,19 +341,20 @@ with demo:
             outputs=[recorded_output, recorded_html_info],
         )
 
-        url_button.click(
-            process_url,
-            inputs=[
-                language_radio,
-                model_dropdown,
-                decoding_method_radio,
-                num_active_paths_slider,
-                url_textbox,
-            ],
-            outputs=[url_output, url_html_info],
-        )
+        with gr.TabItem("Results"):
+            filename_input = gr.inputs.File(label="Upload .txt file", optional=True)
+            textbox_input = gr.inputs.Textbox(lines=5, placeholder="put all history here")
+            textbox_output = gr.outputs.Textbox(label="Assistant's response")
+            submit_button = gr.Button("Submit")
+
+            submit_button.click(
+                langchain_query,
+                inputs=[filename_input, textbox_input],
+                outputs=[textbox_output],
+            )
 
-    gr.Markdown(description)
+        
+    
 
 torch.set_num_threads(1)
 torch.set_num_interop_threads(1)
@@ -386,6 +366,8 @@ torch._C._set_graph_executor_optimize(False)
 if __name__ == "__main__":
     formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
 
+    # 设置日志的格式和级别。
     logging.basicConfig(format=formatter, level=logging.INFO)
 
+    # 启动Gradio界面。
     demo.launch()