Update app.py

app.py

@@ -1,66 +1,47 @@
 import os
 import gradio as gr
-from langchain import LLMChain, PromptTemplate
+from langchain.chains import LLMChain
+from langchain_core.prompts import PromptTemplate
 from langchain.memory import ConversationBufferMemory
-from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_openai import ChatOpenAI 
 from dotenv import load_dotenv
-from langchain.memory import ConversationBufferMemory
 
 load_dotenv()
-os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
+
+API = os.getenv("API_KEY") 
+os.environ["OPENAI_API_KEY"] = API
+
+print("🔑 OpenAI API Key Loaded:", "✅" if API else "❌ (Missing)")
 
 try:
-    llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0)
-    response = llm.invoke("Hello Gemini, can you hear me?")
+    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))
 
-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:**
+template = """
+You are an advanced code reviewer, vulnerability scanner, and secure coding assistant.
+Analyze the code carefully and follow these steps:
+1. Explain what the code does in simple terms.
+2. Review for quality issues, inefficiencies, bad practices.
+3. Perform a security audit (injection flaws, unvalidated inputs, hard-coded secrets, etc.)
+4. For each issue, rate severity (Low/Medium/High), explain exploitation risk, and give recommendations.
+5. Suggest improvements for readability, maintainability, and scalability.
+ALWAYS treat this as production code and prioritize security, clarity, and performance.
+6. Genreate a report.
+"""
+
+template = template + """
 {chat_history}
-
-**User Input:** {user_message}
-
-**Analysis:**
 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
 )
 
-
-
 memory = ConversationBufferMemory(memory_key="chat_history")
 
 llm_chain = LLMChain(
@@ -70,14 +51,14 @@ llm_chain = LLMChain(
 )
 
 def get_text_response(user_message, history):
-    # LangChain memory handles the history internally
     response = llm_chain.predict(user_message=user_message)
     return response
 
+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."],
+    type='messages'
+)
 
-demo = gr.ChatInterface(get_text_response, examples=["How are you doing?","What is a code vunerability?","What happens if a code is not secure?"], type='messages')
-
-if __name__ == "__main__":
-    demo.launch(share=True)
-
-
+if name == "main":
+    demo.launch(share=True)