Inferenced the gpt-4o-mini model

main.py

@@ -21,7 +21,7 @@ business_interaction_graph = BusinessInteractionChatbot()
 brainstrom_graph = brainstroming_graph()
 
 stored_data={}
-# stored_data['business_details']={"business_type": "restaurant", "platform": "instagram", "target_audience": "youths", "business_goals": "to go global", "offerings": "nepali foods", "Challenges_faced": "finding new customers, attracting large customers"}
+stored_data['business_details']={"business_type": "restaurant", "platform": "instagram", "target_audience": "youths", "business_goals": "to go global", "offerings": "nepali foods", "Challenges_faced": "finding new customers, attracting large customers"}
 
 class UserMessage(BaseModel):
     message: str
@@ -46,8 +46,6 @@ def context_analysis(msg: UserMessage):
 @app.post("/business-interaction")
 def business_interaction(interaction: str):
     response,business_details = business_interaction_graph.chat(interaction , stored_data['business_details'])
-    if stored_data['business_details']!=business_details:
-        save_to_db(business_details)
     stored_data['business_details']=business_details
     return {'response': response}
 

requirements.txt

@@ -14,4 +14,5 @@ python-multipart
 langmem
 streamlit
 requests
+langchain_openai
 

utils/models_loader.py

@@ -1,4 +1,5 @@
 from langchain_groq import ChatGroq
+from langchain_openai import ChatOpenAI
 from sentence_transformers import SentenceTransformer
 from huggingface_hub import InferenceClient
 from huggingface_hub import login
@@ -15,11 +16,18 @@ os.environ['GROQ_API_KEY']=os.getenv('GROQ_API_KEY')
 
 llm = ChatGroq(
     model="llama-3.3-70b-versatile",
-    temperature=0,
+    temperature=0.3,
     max_tokens=500,
 
 )
 
+# llm = ChatOpenAI(
+#     model="gpt-4o-mini",
+#     temperature=0,
+#     max_tokens=500,
+# )
+
+
 class HFEmbeddingAPI:
     def __init__(self, api_url, token):
         self.api_url = api_url