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-4", 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()