Update app.py

app.py

@@ -5,19 +5,13 @@ import torch
 from langgraph.graph import StateGraph, START, END
 from langchain.schema import HumanMessage
 from langchain_groq import ChatGroq
-from langsmith import traceable
 from typing import TypedDict
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-# ✅ Load API keys from Google Colab secrets
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")  # Get from Hugging Face secrets
+# ✅ Load API keys from Hugging Face Secrets
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")  # Hugging Face Environment Variable
 LANGSMITH_API_KEY = os.getenv("LANGSMITH_API_KEY")
 
-# ✅ Set environment variables
-os.environ["GROQ_API_KEY"] = GROQ_API_KEY
-os.environ["LANGCHAIN_TRACING_V2"] = "true"
-os.environ["LANGCHAIN_API_KEY"] = LANGSMITH_API_KEY
-
 # ✅ Initialize Groq LLM (for content generation)
 llm = ChatGroq(groq_api_key=GROQ_API_KEY, model_name="mixtral-8x7b-32768")
 
@@ -33,10 +27,9 @@ class State(TypedDict):
     language: str
 
 # ✅ Function to generate multiple blog titles using Groq
-@traceable(name="Generate Titles")
 def generate_titles(data):
     topic = data.get("topic", "")
-    prompt = f"Generate **three short and catchy blog titles** for the topic: {topic}. Each title should be under 10 words. Separate them with new lines."
+    prompt = f"Generate three short and catchy blog titles for the topic: {topic}. Each title should be under 10 words. Separate them with new lines."
     
     response = llm([HumanMessage(content=prompt)])
     titles = response.content.strip().split("\n")  # Get three titles as a list
@@ -44,7 +37,6 @@ def generate_titles(data):
     return {"titles": titles, "selected_title": titles[0]}  # Default to first title
 
 # ✅ Function to generate blog content with tone using Groq
-@traceable(name="Generate Content")
 def generate_content(data):
     title = data.get("selected_title", "")
     tone = data.get("tone", "Neutral")
@@ -54,7 +46,6 @@ def generate_content(data):
     return {"content": response.content.strip()}
 
 # ✅ Function to generate summary using Groq
-@traceable(name="Generate Summary")
 def generate_summary(data):
     content = data.get("content", "")
     prompt = f"Summarize this blog post in a short and engaging way: {content}"
@@ -81,7 +72,6 @@ language_codes = {
 }
 
 # ✅ Function to translate blog content using NLLB-200
-@traceable(name="Translate Content")
 def translate_content(data):
     content = data.get("content", "")
     language = data.get("language", "English")
@@ -141,6 +131,21 @@ def make_blog_generation_graph():
 
     return graph_workflow.compile()
 
+# ✅ Function to generate blog content (Missing function added)
+def generate_blog(topic, tone, language):
+    try:
+        if not topic:
+            return "⚠️ Please enter a topic.", "", "", "", ""
+
+        blog_agent = make_blog_generation_graph()
+        result = blog_agent.invoke({"topic": topic, "tone": tone, "language": language})
+
+        return result["titles"], result["selected_title"], result["content"], result["summary"], result["translated_content"]
+
+    except Exception as e:
+        error_message = f"⚠️ Error: {str(e)}\n{traceback.format_exc()}"
+        return error_message, "", "", "", ""
+
 # ✅ Gradio Interface with "Why Translate?" Section
 with gr.Blocks() as app:
     gr.Markdown(