Update app.py

app.py

@@ -1,12 +1,10 @@
 import streamlit as st
 import faiss
 from sentence_transformers import SentenceTransformer
-import groq  # Hypothetical Groq Python SDK
+from groq import Groq
 
 # Initialize Groq API
-groq_api_key = "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"
-groq_base_url = "https://api.groq.ai/v1"
-client = groq.Client(api_key=groq_api_key, base_url=groq_base_url)
+client = Groq(api_key="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")  # Ensure your API key is valid
 
 # Initialize Sentence Transformer
 embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
@@ -35,10 +33,11 @@ def embed_and_store(chunks):
     embeddings = embedding_model.encode(chunks)
     index.add(embeddings)
 
-# Query handling using the Groq model
+# Query handling using Groq's streaming completions
 def query_llm(prompt):
-    response = client.chat.completions.create(
-        model="groq-llm-model",  # Replace with the actual Groq model identifier
+    # Create a completion request using the Groq model
+    completion = client.chat.completions.create(
+        model="deepseek-r1-distill-llama-70b",  # Use the provided Groq model
         messages=[
             {
                 "role": "system",
@@ -50,10 +49,18 @@ def query_llm(prompt):
             },
             {"role": "user", "content": prompt},
         ],
-        temperature=0.7,
-        max_tokens=350,
+        temperature=0.6,
+        max_completion_tokens=1024,
+        top_p=0.95,
+        stream=True,
+        reasoning_format="raw"
     )
-    return response.choices[0].message.content
+    
+    # Stream and collect the response
+    full_response = ""
+    for chunk in completion:
+        full_response += chunk.choices[0].delta.content or ""
+    return full_response
 
 # Streamlit App
 st.title("AI Relationship Counsellor")