Upload 2 files

app.py

@@ -0,0 +1,113 @@
+import streamlit as st
+from langchain.chat_models import ChatOpenAI
+from langchain.llms import OpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+KEY=os.getenv("OPENAI_API_KEY")
+llm=ChatOpenAI(openai_api_key=KEY,model_name="gpt-3.5-turbo", temperature=0.3)
+
+TEMPLATE="""
+You are an expert in coding and solving problem. kindly help and solving problem. Iam learning how to code and want to see multiple approaches to solve problem. Please Code for {text} with both brute force and optimized approach, with clear time and space complexity of each, in {language}. Include explanation: {explain}
+"""
+
+EXPLANATION_TEMPLATE="""
+You are an expert programmer. Kindly explain this code: {text}
+"""
+
+DEBUG_TEMPLATE="""
+You are an expert coder. Kindly debug this code: {text}
+"""
+
+def generate_code(text, language, explain):
+    code_generation_prompt = PromptTemplate(
+        input_variables=["text", "language", "explain"],
+        template=TEMPLATE
+    )
+    code_chain = LLMChain(llm=llm, prompt=code_generation_prompt, output_key="code", verbose=True)
+    response = code_chain(
+        {
+            "text": text,
+            "language": language,
+            "explain": explain,
+        }
+    )
+    return response["code"]
+
+def generate_explanation(text):
+    explanation_prompt = PromptTemplate(
+        input_variables=["text"],
+        template=EXPLANATION_TEMPLATE
+    )
+    explanation_chain = LLMChain(llm=llm, prompt=explanation_prompt, output_key="explanation", verbose=True)
+    response = explanation_chain(
+        {
+            "text": text,
+        }
+    )
+    return response["explanation"]
+
+def debug_code(text):
+    debug_prompt = PromptTemplate(
+        input_variables=["text"],
+        template=DEBUG_TEMPLATE
+    )
+    debug_chain = LLMChain(llm=llm, prompt=debug_prompt, output_key="debug_info", verbose=True)
+    response = debug_chain(
+        {
+            "text": text,
+        }
+    )
+    return response["debug_info"]
+
+# Streamlit app
+st.title("Coding Assistant")
+
+# User selects the operation
+operation = st.selectbox("What do you want to do?", ["Code Generation", "Code Explanation", "Code Debugging"])
+
+# User inputs
+text = st.text_input("Enter the problem statement:")
+
+
+if operation == "Code Generation":
+    language = st.selectbox("Select the programming language:", ["C", "C++", "Java", "Python", "JavaScript", "Go"])
+    explain = st.checkbox("Include explanation")
+    if text:
+        code = generate_code(text, language, explain)
+        
+        # Save history
+        if 'history' not in st.session_state:
+            st.session_state['history'] = []
+        st.session_state['history'].append(code)
+        
+        # Display generated code
+        st.subheader("Generated Code:")
+        st.write(code, language=language)
+        
+        # Add download button for the code
+        st.download_button(
+            label="Download Code",
+            data=code,
+            file_name='code.txt',
+            mime='text/plain',
+        )
+        
+        # Display history
+        st.subheader("History:")
+        for i, item in enumerate(st.session_state['history']):
+            st.code(item, language=language)
+
+elif operation == "Code Explanation":
+    if text:  # Check if the user has entered a problem statement
+        explanation = generate_explanation(text)
+        st.subheader("Generated Explanation:")
+        st.write(explanation)
+elif operation == "Code Debugging":
+    if text:  # Check if the user has entered a problem statement
+        debug_info = debug_code(text)
+        st.subheader("Debug Information:")
+        st.write(debug_info)

requirements.txt

@@ -0,0 +1,4 @@
+openai
+langchain
+streamlit
+python-dotenv