Upload advisor.py

advisor.py

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+import os, config, requests
+import gradio as gr
+import pandas as pd
+import numpy as np
+from openai.embeddings_utils import get_embedding, cosine_similarity
+import openai
+openai.api_key = config.OPENAI_API_KEY
+
+messages = [{"role": "system", "content": 'You are a telecom advisor. Respond to all input in 50 words in dictionary format  .'}]
+
+# prepare Q&A embeddings dataframe
+question_df = pd.read_csv('data/questions_with_embeddings.csv')
+question_df['embedding'] = question_df['embedding'].apply(eval).apply(np.array)
+
+def transcribe(audio):
+    global messages, question_df
+
+    # API now requires an extension so we will rename the file
+    audio_filename_with_extension = audio + '.wav'
+    os.rename(audio, audio_filename_with_extension)
+
+    audio_file = open(audio_filename_with_extension, "rb")
+    transcript = openai.Audio.transcribe("whisper-1", audio_file)
+
+    question_vector = get_embedding(transcript['text'], engine='text-embedding-ada-002')
+
+    question_df["similarities"] = question_df['embedding'].apply(lambda x: cosine_similarity(x, question_vector))
+    question_df = question_df.sort_values("similarities", ascending=False)
+
+    best_answer = question_df.iloc[0]['answer']
+
+    user_text = f"Using the following text, answer the question '{transcript['text']}'. {config.ADVISOR_CUSTOM_PROMPT}: {best_answer}" 
+    messages.append({"role": "user", "content": user_text})
+
+    response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages)
+
+    system_message = response["choices"][0]["message"]
+    print(system_message)
+    messages.append(system_message)
+
+    # text to speech request with eleven labs
+    url = f"https://api.elevenlabs.io/v1/text-to-speech/{config.ADVISOR_VOICE_ID}/stream"
+    data = {
+        "text": system_message["content"].replace('"', ''),
+        "voice_settings": {
+            "stability": 0.1,
+            "similarity_boost": 0.8
+        }
+    }
+
+    r = requests.post(url, headers={'xi-api-key': config.ELEVEN_LABS_API_KEY}, json=data)
+
+    output_filename = "reply.mp3"
+    with open(output_filename, "wb") as output:
+        output.write(r.content)
+
+    chat_transcript = ""
+    for message in messages:
+        if message['role'] != 'system':
+            chat_transcript += message['role'] + ": " + message['content'] + "\n\n"
+
+    # return chat_transcript
+    return chat_transcript, output_filename
+
+
+# set a custom theme
+theme = gr.themes.Default().set(
+    body_background_fill="#000000",
+)
+
+with gr.Blocks(theme=theme) as ui:
+    # advisor image input and microphone input
+    advisor = gr.Image(value=config.ADVISOR_IMAGE).style(width=config.ADVISOR_IMAGE_WIDTH, height=config.ADVISOR_IMAGE_HEIGHT)
+    audio_input = gr.Audio(source="microphone", type="filepath")
+
+    # text transcript output and audio 
+    text_output = gr.Textbox(label="Conversation Transcript")
+    audio_output = gr.Audio()
+
+    btn = gr.Button("Run")
+    btn.click(fn=transcribe, inputs=audio_input, outputs=[text_output, audio_output])
+
+ui.launch(debug=True, share=True)