Update app.py

app.py

@@ -1,10 +1,6 @@
 import os
 import gradio as gr
-import re
 from gradio.components import ChatMessage
-from huggingface_hub import InferenceClient
-
-
 import openai
 from pydantic import BaseModel
 from langchain_openai import ChatOpenAI
@@ -18,9 +14,11 @@ if os.getenv("HF_SPACE", None) is None:
     from dotenv import load_dotenv
     load_dotenv()
 
+# API Keys
 OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
 openai.api_key = OPENAI_API_KEY
 
+# response structure
 class ResearchResponse(BaseModel):
     topic: str
     empathetic_response: str
@@ -30,11 +28,11 @@ class ResearchResponse(BaseModel):
     sources: list[str]
     tools_used: list[str]
 
-# 1) set up LLM + parser
+# set up LLM and parser
 llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY, model="gpt-4o-mini")
 parser = PydanticOutputParser(pydantic_object=ResearchResponse)
 
-# 2) your custom prompt
+# custom prompt to API
 prompt = ChatPromptTemplate.from_messages([
     (
             "system",
@@ -61,20 +59,19 @@ prompt = ChatPromptTemplate.from_messages([
 ])
 prompt = prompt.partial(format_instructions=parser.get_format_instructions())
 
-# 3) wire up your tools + agent
+# set up tool and agent
 tools = [save_tool]
 agent = create_tool_calling_agent(llm=llm, prompt=prompt, tools=tools)
 agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=False)
 
-# 4) the Gradio chat function
 
-client = InferenceClient(token=os.environ["HUGGINGFACEHUB_API_TOKEN"])
+# Gradio chat response function
 
 def respond(message, history: list[dict]):
 
     agent_output = agent_executor.invoke({
         "query": message,
-        "chat_history": history #[{"role": m.role, "content": m.content} for m in history]
+        "chat_history": history 
     })
 
     raw = agent_output["output"]
@@ -89,7 +86,6 @@ def respond(message, history: list[dict]):
             out.question,
         ])
     except Exception as e:
-        # Fallback: just use the raw text if JSON parsing fails
         print("Fallback to exception: raw")
         assistant_text = raw
 
@@ -98,13 +94,13 @@ def respond(message, history: list[dict]):
     yield response 
 
 
-# 5) launch ChatInterface
+# launch ChatInterface
 demo = gr.ChatInterface(
     respond,
     title="YAQIN Chatbot",
     description="Culturally Sensitive Chatbot for Muslim Women Wanting Mental Healthcare",
-    type="messages",   # ensures we expect ChatMessage objects
+    type="messages",
 )
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch()