Update app.py

app.py

@@ -4,7 +4,7 @@ import numpy as np
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 import gradio as gr
-from groq import Groq
+from together import Together
 
 # Load pre-trained Sentence Transformer model
 model = SentenceTransformer('LaBSE')
@@ -15,18 +15,23 @@ 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"))
+# Together API setup
+client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
 
 def llama_query(prompt, system_content):
     response = client.chat.completions.create(
+        model="meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
         messages=[
             {"role": "system", "content": system_content},
             {"role": "user", "content": prompt}
         ],
-        model="llama-3.1-70b-versatile",
-        max_tokens=800,
-        temperature=0.7
+        max_tokens=512,
+        temperature=0.7,
+        top_p=0.7,
+        top_k=50,
+        repetition_penalty=1,
+        stop=["<|eot_id|>", "<|eom_id|>"],
+        stream=False
     )
     return response.choices[0].message.content
 
@@ -64,18 +69,18 @@ def get_answer(user_question, threshold=0.35):
         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']