bots/feedback.py

import os
import json
import requests
from llamaapi import LlamaAPI
from openai import OpenAI
from PIL import Image
from transformers import TrOCRProcessor, VisionEncoderDecoderModel

# Initialize
llama = LlamaAPI("LL-AirERHEk0jLIE1yEPvMXeobNfLsqLWJWcxLRS53obrZ3XyqMTfZc4EAuOs7r3wso")

api_key = "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"
os.environ["OPENAI_API_KEY"] = api_key
client = OpenAI()


# Feedbacks chatbot.
def transcribe_handwriting(image_path):
    """
    Transcribes text from a handwriting image located at a local path using TrOCR.

    Parameters:
    - image_path (str): The local path to the handwriting image file.

    Returns:0000000000000
    - str: The transcribed text. Returns False if transcription fails.
    """
    try:
        url = image_path
        image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

        processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten')
        model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten')
        pixel_values = processor(images=image, return_tensors="pt").pixel_values

        generated_ids = model.generate(pixel_values)
        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]# Prepare the image for the model

        return generated_text
    except Exception as e:
        print(f"An error occurred while processing the image: {e}")
        return f"An error occurred while processing the image: {e}"


def chat_with_model(prompt, model_type="gpt-4"):
    """
    Function to chat with either the GPT-4 or LLaMA model.

    Parameters:
    - prompt (str): The message or question to send to the model.
    - model_type (str): The type of model to use ("gpt-4" or "llama").

    Returns:
    - str: The response from the selected model.
    """
    if model_type == "gpt-4":
        try:
            chat_completion = client.chat.completions.create(
                model="gpt-4",
                messages=[
                    {"role": "system", "content": "Provide feedback on the inputted writing sample from an ESL learner. "
                      "Focus on areas such as grammar, vocabulary usage, and overall coherence and organization of the essay. "
                      "Offer corrective feedback on errors, suggest improvements, and highlight positive aspects to encourage "
                      "the learner. Please ensure the feedback is constructive, clear, and supportive to help the learner "
                      "understand and apply the suggestions. Always frame feedback in a positive, constructive manner. "
                      "Focus on how the student can improve rather than just highlighting mistakes. Provide clear examples "
                      "when pointing out errors or suggesting improvements. Prompt the learner to reflect on specific parts of "
                      "their writing"},
                    {"role": "user", "content": prompt},
                ]
            )
            return chat_completion.choices[0].message.content.strip()
        except Exception as e:
            return f"An error occurred with GPT-4: {e}"
    elif model_type.startswith("llama"):
        api_request_json = {
            "model": model_type,  # This can be "llama-7b", "llama-13b", etc.
            "messages": [
                {"role": "system", "content": "Provide feedback on the inputted writing sample from an ESL learner. "
                      "Focus on areas such as grammar, vocabulary usage, and overall coherence and organization of the essay. "
                      "Offer corrective feedback on errors, suggest improvements, and highlight positive aspects to encourage "
                      "the learner. Please ensure the feedback is constructive, clear, and supportive to help the learner "
                      "understand and apply the suggestions. Always frame feedback in a positive, constructive manner. "
                      "Focus on how the student can improve rather than just highlighting mistakes. Provide clear examples "
                      "when pointing out errors or suggesting improvements. Prompt the learner to reflect on specific parts of "
                      "their writing"},
                {"role": "user", "content": prompt},
            ]
        }
        try:
            response = llama.run(api_request_json)
            response_data = response.json()
            return response_data["choices"][0]["message"]["content"]
        except Exception as e:
            return f"An error occurred with LLaMA: {e}"
    else:
        return "Unsupported model type."


def feedbacks_main():
    print("Hello! I am a chatbot. Which model would you like to use? (llama-13b-chat, gpt-4, gpt-3.5-turbo)")
    model_type = input("Choose model or type 'exit' to quit: ").strip()

    if model_type.lower() == "exit":
        print("Thanks for using! Wish you all the best in English learning!")
        return

    while model_type not in ["llama-13b-chat", "gpt-4", "gpt-3.5-turbo"]:
        print("Invalid model. Please copy one from (llama-13b-chat, gpt-4, gpt-3.5-turbo) or type 'exit' to quit.")
        model_type = input("Choose model: ").strip()
        if model_type.lower() == "exit":
            print("Thanks for using! Wish you all the best in English learning!")
            return

    print(f"Model set to {model_type}. How can I assist you today?\n")

    user_inputs = []  # List to store all user inputs
    outputs = []  # List to store all chatbot responses
    interaction_history = []  # List to store the full interaction history (both inputs and responses)

    while True:
        upload_decision = input("Do you need to upload a picture for transcription? (yes/no): ").strip().lower()
        user_input = ""
        if upload_decision == "yes":
            image_path = input("Please provide the link to your handwriting image: \n")
            generated_text = transcribe_handwriting(image_path)
            if not generated_text:
                print("Failed to transcribe the image or no text was found.")
            else:
                print("Transcribed text:", generated_text)
                user_input = generated_text
        if not user_input:
            user_input = input("You: ")
            if user_input.lower() == "exit":  # Check for 'exit' command to break the loop
                print("Thanks for using! Wish you all the best in English learning!")
                break  # Exit the while loop

        user_inputs.append(user_input)  # Add user input to the list
        interaction_history.append(f"You: {user_input}")  # Add user input to the interaction history

        response = chat_with_model(user_input, model_type)
        outputs.append(response)  # Add chatbot response to the list
        interaction_history.append(f"Chatbot: {response}")  # Add chatbot response to the interaction history

        print("Chatbot:", response)

    return user_inputs, outputs, interaction_history