Create app.py

app.py

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+import os
+import pandas as pd
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import gradio as gr
+from groq import Groq
+
+# Load pre-trained Sentence Transformer model
+model = SentenceTransformer('LaBSE')
+
+# 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())
+
+# Groq API setup
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+def llama_query(prompt, system_content):
+    response = client.chat.completions.create(
+        messages=[
+            {"role": "system", "content": system_content},
+            {"role": "user", "content": prompt}
+        ],
+        model="llama3-70b-8192",
+        max_tokens=800,
+        temperature=0.7
+    )
+    return response.choices[0].message.content
+
+def check_blood_donation_relevance(question):
+    prompt = f"Is the following question related to blood donation? Answer with 'Yes' or 'No': {question}"
+    system_content = "You are an assistant that determines if a question is related to blood donation."
+    response = llama_query(prompt, system_content)
+    return response.strip().lower() == 'yes'
+
+def detect_language(text):
+    prompt = f"Detect the language of this text. If it's Swahili, return 'Swahili'. If it's English, return 'English'. Here's the text: {text}"
+    system_content = "You are a language detection assistant."
+    response = llama_query(prompt, system_content)
+    return response.strip().lower()
+
+def translate_to_english(text):
+    prompt = f"Translate the following Swahili text to English: {text}"
+    system_content = "You are a translation assistant that translates from Swahili to English."
+    response = llama_query(prompt, system_content)
+    return response
+
+def translate_to_swahili(text):
+    prompt = f"Translate the following text to simple Swahili, avoiding difficult words: {text}"
+    system_content = "You are a translation assistant that translates to simple Swahili."
+    response = llama_query(prompt, system_content)
+    return response
+
+def refine_answer(question, retrieved_answer):
+    prompt = f"Question: {question}\nRetrieved Answer: {retrieved_answer}\nPlease refine the retrieved answer according to the question asked, ensuring it's clear and concise."
+    system_content = "You are an assistant that refines answers to make them more relevant and natural."
+    return llama_query(prompt, system_content)
+
+def get_answer(user_question, threshold=0.35):
+    if not check_blood_donation_relevance(user_question):
+        return "I'm sorry, but your question doesn't seem to be related to blood donation. Could you please ask a question about blood donation?", 0
+
+    language = detect_language(user_question)
+    
+    if language == 'swahili':
+        english_question = translate_to_english(user_question)
+    else:
+        english_question = user_question
+
+    user_embedding = model.encode(english_question)
+    
+    similarities = cosine_similarity([user_embedding], question_embeddings)
+    
+    max_similarity = np.max(similarities)
+    
+    if max_similarity > threshold:
+        similar_question_idx = np.argmax(similarities)
+        retrieved_answer = df.iloc[similar_question_idx]['Answer']
+        refined_answer = refine_answer(english_question, retrieved_answer)
+
+        if language == 'swahili':
+            refined_answer = translate_to_swahili(refined_answer)
+
+        return refined_answer, max_similarity
+    else:
+        default_message = "The system couldn't find a sufficient answer to your question. Do you want to learn anything else about blood donation?"
+        if language == 'swahili':
+            default_message = translate_to_swahili(default_message)
+        return default_message, max_similarity
+
+# Gradio app
+def gradio_app(user_question):
+    answer, similarity = get_answer(user_question)
+    return f"Similarity: {similarity:.2f}\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 in English or Swahili.",
+)
+
+iface.launch()