Create app.py

app.py

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+#
+
+
+# openai.api_key = "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"
+# set_api_key("5d879e23b3f825be89038d704485ea5a")
+import openai
+
+from elevenlabs import set_api_key
+
+set_api_key("24efc219fc704fe895cee524fc66d529")
+
+openai.api_key = "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"
+
+import whisper
+
+model = whisper.load_model("base")
+import os
+os.environ['PATH'] += os.pathsep + '/path/to/mpv/'
+from typing import Optional
+
+
+class Chat:
+
+    def __init__(self , system: Optional[str] = None):
+        self.system = system
+        self.messages = []
+
+        if system is not None:
+            self.messages.append({
+                "role": "system",
+                "content": system
+            })
+
+    def prompt(self, content: str) -> str:
+        self.messages.append({
+            "role": "user",
+            "content": content
+        })
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=self.messages
+        )
+        response_content = response["choices"][0]["message"]["content"]
+        self.messages.append({
+            "role": "assistant",
+            "content": response_content
+        })
+        return response_content
+
+
+from elevenlabs import generate, play , stream
+
+import gradio as gr
+
+def read_and_print_file(file_path):
+    with open(file_path, 'r') as file:
+        return  file.read()
+
+
+# Example usage:
+file_path = 'the_interview_questions'
+contents = read_and_print_file(file_path)
+
+
+chat = Chat(system="""You have to take the annual financial review. To ensure the user have the most up-to-date information, you have to ask a few questions.
+Make Sure the following information is being extracted after the Interview:(Ask the questions by one by one not in a big chunk, ask the question and wait for his response and then ask the second questions)
+Ask question in your own way that we can know that (ask the questions one by one slowly don't rush and wait for user repsponse for first question and then ask the second question)
+remember to ask the below questions one after one like a interview not all at a time.
+wait for user response after one single questions until then don't ask next question
+remember to not to include question number.
+""" + contents + """
+""")
+
+
+
+
+def convert_tuples_to_list(tuples_list):
+    result = []
+    conversation = ""
+    for tuple_item in tuples_list:
+        result.append(tuple_item[0])  # Append question
+        result.append(tuple_item[1])  # Append answer
+    for i in result:
+        conversation = conversation + i + " \n"
+    return conversation
+
+
+def run_text_prompt(message, chat_history):
+    bot_message = chat.prompt(content=message)
+
+    audio = generate(
+        text=bot_message,
+        voice="Antoni",
+    )
+
+    play(audio)
+
+    chat_history.append((message, bot_message))
+    return "", chat_history
+
+
+def run_audio_prompt(audio, chat_history):
+    if audio is None:
+        return None, chat_history
+
+    message_transcription = model.transcribe(audio)["text"]
+    _, chat_history = run_text_prompt(message_transcription, chat_history)
+    return None, chat_history
+file_path = "myfile.txt"
+
+try:
+  with open(file_path, "r") as file:
+    file_contents = file.read()
+
+except FileNotFoundError:
+  print(f"File not found at: {file_path}")
+except IOError:
+  print(f"Error reading file: {file_path}")
+
+def process_text(conversation, text):
+  # print(text)
+  completion = openai.ChatCompletion.create(model="gpt-3.5-turbo",
+                                            messages=[{
+                                              "role":
+                                              "user",
+                                              "content":
+                                              conversation + text
+                                            }])
+  # completion = text;
+  # print(completion)
+  # print("Your 710 words has been translated. ")
+  print(completion.choices[0].message.content.strip())
+  return completion.choices[0].message.content.strip()
+
+def inference(text):
+    html = (
+            "<div >"
+            "<img  src='C://Users//l//Desktop//pythonProject33//thumbnail_IAI_icon.png' alt='image One'>"
+            + "</div>"
+    )
+    return html
+with gr.Blocks(title="hi") as app2:
+    gr.HTML("""<img src="C://Users//l//Desktop//pythonProject33//thumbnail_IAI_icon.png" alt="A beautiful landscape">""")
+    gr.Markdown("""<h1><center>Interview Phase 1</center></h1>
+                    <img src="thumbnail_IAI_icon.png" >""")
+    chatbot = gr.Chatbot()
+
+    msg = gr.Textbox()
+    msg.submit(run_text_prompt, [msg, chatbot], [msg, chatbot])
+
+    with gr.Row():
+        audio = gr.Audio(source="microphone", type="filepath")
+
+        fn = run_audio_prompt,
+        inputs = [audio, chatbot],
+        outputs = [audio, chatbot]
+
+
+        # fn = run_audio_prompt,
+        # inputs = [audio, chatbot],
+        # outputs = [audio, chatbot]
+
+        send_audio_button = gr.Button("Send Audio", interactive=True)
+        send_audio_button.click(run_audio_prompt, [audio, chatbot], [audio, chatbot])
+
+demo = gr.TabbedInterface([app2], [ "Interview"])
+
+demo.launch(share=False,
+                        debug=False,
+                        server_name="localhost")
+