Create utils.py

utils.py

@@ -0,0 +1,165 @@
+import openai # type: ignore
+from openai import OpenAI
+from dotenv import load_dotenv, find_dotenv # type: ignore
+from huggingface_hub import InferenceClient # type: ignore
+import pandas as pd # type: ignore
+import os, time
+
+load_dotenv(find_dotenv())
+# Setup API keys
+openai.api_key = os.getenv("OPENAI_API_KEY")
+os.environ["HF_TOKEN"] = os.getenv("HF_TOKEN")
+
+client = OpenAI()
+
+# Define a few-shot prompt for personality prediction
+few_shot_prompt = """
+You are an expert in personality psychology. Based on the text provided, predict the personality scores for the Big Five personality traits: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Each score should be a floating-point number between 0 and 1.
+
+Example 1:
+Text: "I love exploring new ideas and trying new things."
+Scores: Openness: 0.9, Conscientiousness: 0.4, Extraversion: 0.7, Agreeableness: 0.5, Neuroticism: 0.3
+
+Example 2:
+Text: "I prefer to plan everything in advance and stick to the plan."
+Scores: Openness: 0.3, Conscientiousness: 0.8, Extraversion: 0.4, Agreeableness: 0.6, Neuroticism: 0.4
+
+Now, predict the scores for the following text:
+"""
+
+def predict_personality(text):
+    # Prepare the prompt with the user's text
+    prompt = few_shot_prompt + f"Text: \"{text}\"\nScores:"
+    
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": prompt}
+    ]
+    
+    # Call the OpenAI API to get the prediction
+    response = openai.chat.completions.create(
+        model="gpt-4",
+        messages=messages,
+        max_tokens=50,
+        temperature=0.5
+    )
+    
+    # Extract the predicted scores from the response
+    scores_text = response.choices[0].message.content.strip()
+    scores = [float(score.split(":")[1].strip()) for score in scores_text.split(",")]
+    return scores
+
+def create_line_plot(scores):
+    labels = ['Openness', 'Conscientiousness', 'Extraversion', 'Agreeableness', 'Neuroticism']
+    data = {'Personality': labels, 'Score': scores}
+    return pd.DataFrame(data)
+
+# Gradio interface
+def personality_app(text):
+    scores = predict_personality(text)
+    df = create_line_plot(scores)
+    return df
+
+
+def transcribe_audio(audio_path):
+    with open(audio_path, "rb") as audio_file:
+        transcript = client.audio.transcriptions.create(model="whisper-1", file=audio_file)
+        return transcript.text
+
+def openai_chat_completion(messages: list, selected_model: str) -> list[str]:
+    try:
+        response = openai.chat.completions.create(
+            # model='gpt-3.5-turbo',
+            model=selected_model,
+            messages=messages,
+            # temperature=0.5,
+        )
+        collected_messages = response.choices[0].message.content.strip().split('\n')
+        return collected_messages   # return all the collected chunks of messages
+    
+    except Exception as e:
+        return [str(e)]
+
+def llama2_chat_completion(messages: list, hf_model_id: str, selected_model: str) -> list[str]:
+    try:
+        hf_token = os.getenv("HF_TOKEN")
+        client = InferenceClient(model=hf_model_id, token=hf_token)
+        # Start the chat completion process with streaming enabled
+        response_stream = client.chat_completion(messages, max_tokens=400, stream=True)
+
+        # Collect the generated message chunks
+        collected_messages = []
+        for completion in response_stream:
+            # Assuming the response structure is similar to OpenAI's
+            delta = completion['choices'][0]['delta']
+            if 'content' in delta.keys():
+                collected_messages.append(delta['content'])
+        # Return the collected messages
+        return collected_messages
+    
+    except Exception as e:
+        return [str(e)]
+
+def generate_messages(messages: list) -> list:
+    formatted_messages = [   # first format of messages for chat completion
+        {
+            'role': 'system',
+            'content': 'You are a helpful assistant.'
+        }
+        
+    ]
+    for m in messages:   # Loop over the existing chat history and create user, assistant responses.
+        formatted_messages.append({
+            'role': 'user',
+            'content': m[0]
+        })
+        if m[1] != None:
+            formatted_messages.append({
+                'role': 'assistant',
+                'content': m[1]
+            })
+    return formatted_messages
+
+def generate_audio_response(chat_history: list, selected_model: str) -> list:  # type: ignore
+    messages = generate_messages(chat_history)  # generates messages based on chat history
+    if selected_model == "gpt-4" or "gpt-3.5-turbo":
+        bot_message = openai_chat_completion(messages, selected_model) # Get all the collected chunks of messages for streaming
+    if selected_model == "Llama-3-8B":
+        hf_model_id = "meta-llama/Meta-Llama-3-8B"
+        bot_message = llama2_chat_completion(messages, hf_model_id, selected_model)
+    if selected_model == "Llama-2-7b-chat-Counsel-finetuned":
+        hf_model_id = "TVRRaviteja/Llama-2-7b-chat-Counsel-finetuned"
+        bot_message = llama2_chat_completion(messages, hf_model_id, selected_model)
+    else:
+        selected_model='gpt-3.5-turbo'
+        bot_message = openai_chat_completion(messages, selected_model)
+    
+    chat_history[-1][1] = ''        # [-1] -> last conversation, [1] -> current carebot message
+    for bm in bot_message:     # Loop over the collected messages
+        chat_history[-1][1] += bm
+        time.sleep(0.05)
+        yield chat_history   # For streamed carebot responses
+
+def generate_text_response(chat_history: list, selected_model: str) -> list:  # type: ignore
+    messages = generate_messages(chat_history) # generates messages based on chat history
+    if selected_model == "gpt-4" or "gpt-3.5-turbo":
+        bot_message = openai_chat_completion(messages, selected_model) # Get all the collected chunks of messages for streaming
+    if selected_model == "Llama-3-8B":
+        hf_model_id = "meta-llama/Meta-Llama-3-8B"
+        bot_message = llama2_chat_completion(messages, hf_model_id, selected_model)
+    if selected_model == "Llama-2-7b-chat-Counsel-finetuned":
+        hf_model_id = "TVRRaviteja/Llama-2-7b-chat-Counsel-finetuned"
+        bot_message = llama2_chat_completion(messages, hf_model_id, selected_model)
+    else:
+        selected_model='gpt-3.5-turbo'
+        bot_message = openai_chat_completion(messages, selected_model)
+        
+    chat_history[-1][1] = ''        # [-1] -> last conversation, [1] -> current carebot message
+    for bm in bot_message:     # Loop over the collected messages
+        chat_history[-1][1] += bm
+        time.sleep(0.05)
+        yield chat_history   # For streamed carebot responses
+
+def set_user_response(user_message: str, chat_history: list) -> tuple:
+    chat_history += [[user_message, None]]  #Append the recent user message into the chat history
+    return '', chat_history