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import os
import openai
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import pandas as pd
import gradio as gr
# Load pre-trained Sentence Transformer model
model = SentenceTransformer('all-MiniLM-L6-v2')
# Load questions and answers from the CSV file
df = pd.read_csv('combined_questions_and_answers.csv')
# Encode all questions in the dataset
question_embeddings = model.encode(df['Question'].tolist())
# OpenAI API key setup
openai.api_key = os.getenv("OPENAI_API_KEY")
# Function to call OpenAI API to refine and translate text
def refine_text(prompt):
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo-16k",
messages=[
{"role": "system", "content": "You are an assistant that refines text to make it conversational and natural. If the question is in Swahili, respond in Swahili."},
{"role": "user", "content": prompt}
],
max_tokens=800,
n=1,
stop=None,
temperature=0.7
)
return response['choices'][0]['message']['content']
# Function to find the most similar question and provide the answer
def get_answer(user_question, threshold=0.80):
# Encode the user question
user_embedding = model.encode(user_question)
# Calculate cosine similarities
similarities = cosine_similarity([user_embedding], question_embeddings)
# Find the most similar question
max_similarity = np.max(similarities)
if max_similarity > threshold:
# Get the index of the most similar question
similar_question_idx = np.argmax(similarities)
# Retrieve the corresponding answer
answer = df.iloc[similar_question_idx]['Answer']
# Refine the answer using GPT-4
refined_answer = refine_text(f"Refine this answer: {answer}")
return refined_answer, max_similarity
else:
# Generate an answer using GPT-4 if no similar question is found
refined_answer = refine_text(f"Answer this question: {user_question}")
return refined_answer, max_similarity
# Gradio app
def gradio_app(user_question):
answer, similarity = get_answer(user_question)
return f"Similarity: {similarity}\nAnswer: {answer}"
# Launch the Gradio app
iface = gr.Interface(
fn=gradio_app,
inputs=gr.Textbox(label="Enter your question"),
outputs=gr.Textbox(label="Answer"),
title="Blood Donation Q&A",
description="Ask questions related to blood donation and get answers.",
)
iface.launch() |