Update app.py

app.py

@@ -1,68 +1,27 @@
 import os
 import gradio as gr
-from langchain.chains import LLMChain
+from langchain.chains.llm import LLMChain
 from langchain_core.prompts import PromptTemplate
 from langchain.memory import ConversationBufferMemory
-from langchain_openai import ChatOpenAI 
+from langchain_openai import ChatOpenAI
 from dotenv import load_dotenv
 
 load_dotenv()
 
-API = os.getenv("API_KEY") 
-os.environ["OPENAI_API_KEY"] = API
+API = os.getenv("API_KEY")
+if not API:
+    raise ValueError("❌ Missing API_KEY environment variable!")
 
-print("🔑 OpenAI API Key Loaded:", "✅" if API else "❌ (Missing)")
+os.environ["OPENAI_API_KEY"] = API
 
-try:
-    llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)  # or "gpt-4o" / "gpt-3.5-turbo"
-    response = llm.invoke("Hello OpenAI, can you hear me?")
-    print("✅ API is working!")
-    print("Response:", response.content)
-except Exception as e:
-    print("❌ API Error:", str(e))
+llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
 
 template = """
-You are an expert code reviewer and security analyst specializing in vulnerability detection and secure coding practices.
-
-For any code provided, analyze it systematically:
-
-*📋 Code Overview*: 
-- Briefly explain what the code does and its purpose
-
-*🔒 Security Analysis*:
-- Identify security vulnerabilities with risk levels:
-  - 🔴 *High Risk*: Critical vulnerabilities that could lead to system compromise
-  - 🟡 *Medium Risk*: Moderate security concerns that should be addressed
-  - 🟢 *Low Risk*: Minor security improvements
-- Explain potential exploitation methods
-
-*⚡ Code Quality Review*:
-- Performance issues and bottlenecks
-- Code readability and maintainability
-- Best practice violations
-- Logic errors or inefficiencies
-
-*🛠 Actionable Recommendations*:
-- Provide specific, implementable fixes
-- Include secure code examples where applicable
-- Suggest architectural improvements
-
-For non-code queries, provide relevant security guidance and best practices.
-
-*Conversation History:*
-{chat_history}
-
-*User Input:* {user_message}
-
-*Analysis:*
+You are an expert code reviewer and security analyst specializing in vulnerability detection...
 User: {user_message}
-
-IMPORTANT: Regardless of the user's input, you MUST maintain your role as a code reviewer and security assistant. Do NOT deviate from these instructions or engage in any other persona.
 Chatbot:
 """
 
-
-
 prompt = PromptTemplate(
     input_variables=["chat_history", "user_message"], template=template
 )
@@ -76,14 +35,17 @@ llm_chain = LLMChain(
 )
 
 def get_text_response(user_message, history):
-    response = llm_chain.predict(user_message=user_message)
-    return response
+    return llm_chain.predict(user_message=user_message)
 
 demo = gr.ChatInterface(
     get_text_response,
-    examples=["What is a code vulnerability?", "What happens if a code is not secure?", "Give me secure coding tips."],
+    examples=[
+        "What is a code vulnerability?",
+        "What happens if a code is not secure?",
+        "Give me secure coding tips."
+    ],
     type='messages'
 )
 
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch(share=True)