better response

src/prompt_based/examples.txt

@@ -1,5 +1,20 @@
-Examples:
-================================================================
+Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
+
+You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are AIMessage, HumanMessage, SystemMessage, and ChatMessage -- ChatMessage takes in an arbitrary role parameter. Most of the time, you'll just be dealing with HumanMessage, AIMessage, and SystemMessage.
+
+Most of the time, to use LangChain library, you need these imports below:
+
+from langchain import LLMChain
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+
+Here are the examples:
+================================================================================================================================
 Goal: Generate blog post from title. Generate at least 500 words
 ---------
 Python Code:
@@ -12,11 +27,11 @@ from langchain.prompts.chat import (
 )
 
 def generate_blog_post(title):
-    chat = ChatOpenAI(temperature=0)
+    chat = ChatOpenAI(temperature=0.1) # Here temperature is set a little bit higher to put some variation
 
     template = "You are a helpful assistant that generates a blog post from the title: {title}. Please provide some content."
     system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-    human_template = "{text}"
+    human_template = "{title}"
     human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
     chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
 
@@ -36,7 +51,7 @@ from langchain.prompts.chat import (
 )
 
 def generate_translation(input_language, output_language, text):
-    chat = ChatOpenAI(temperature=0)
+    chat = ChatOpenAI(temperature=0) # Here temperature is set to 0 because translation should be concrete
 
     template = "You are a helpful assistant that translates {input_language} to {output_language}."
     system_message_prompt = SystemMessagePromptTemplate.from_template(template)
@@ -48,7 +63,7 @@ def generate_translation(input_language, output_language, text):
     result = chain.run(input_language=input_language, output_language=output_language, text=text)
     return result # it will return string
 ================================================================
-Goal: Animal name from animal
+Goal: Generate animal name from animal
 ---------
 Python Code:
 from langchain import LLMChain
@@ -60,7 +75,7 @@ from langchain.prompts.chat import (
 )
 
 def generate_animal_name(animal):
-    chat = ChatOpenAI(temperature=0)
+    chat = ChatOpenAI(temperature=0.1) # Here temperature is set a little bit higher to put some variation
 
     template = "You are a helpful assistant that generates a name for an animal. You generate short answer."
     system_message_prompt = SystemMessagePromptTemplate.from_template(template)

src/prompt_based/model.py

@@ -2,12 +2,10 @@
 from streamlit_prompts import *
 from langchain.chat_models import ChatOpenAI
 from langchain.chains import LLMChain
-from subprocess import PIPE, run
+from subprocess import PIPE
 import tempfile
 from termcolor import colored
 import subprocess
-from subprocess import DEVNULL, STDOUT
-import sys
 import shutil
 
 from dotenv import load_dotenv
@@ -33,18 +31,11 @@ def run_python(self,code):
             tmp.write(code)
             tmp.flush()
             environmental_variables = {'OPENAI_API_KEY':self.openai_api_key}
-            #python_path = subprocess.check_output("which python", shell=True).strip().decode('utf-8')
             python_path = shutil.which("python")
             process = subprocess.Popen([python_path,tmp.name], env=environmental_variables,stdout=PIPE, stderr=PIPE)
             output, err = process.communicate()
             return output.strip().decode('utf-8'), err.strip().decode('utf-8')
 
-
-            """command = f"OPENAI_API_KEY={self.openai_api_key} python "+tmp.name
-            print(colored(command,"blue"))
-            result = run(command, stdout=PIPE, stderr=PIPE, universal_newlines=True, shell=True)
-            return result.stdout, result.stderr"""
-
     def refine_code(self,code):
         if "```" in code: 
             code = code.split("```")[1]
@@ -85,6 +76,8 @@ def __call__(self,topic,num_iterations=10):
             if success:
                 break
 
+            response = error
+
             feedback = self.fix_chain.run(code=total_code,error=error)
 
             percentage += 100 // num_iterations
@@ -124,11 +117,7 @@ def run_code(self,code):
         with tempfile.NamedTemporaryFile("w",suffix=".py") as tmp:
             tmp.write(code)
             tmp.flush()
-            command = f"OPENAI_API_KEY={self.openai_api_key} streamlit run "+tmp.name
             environmental_variables = {'OPENAI_API_KEY':self.openai_api_key,"STREAMLIT_SERVER_PORT":"8502"}
-            #python_path = subprocess.check_output("which python", shell=True).strip().decode('utf-8')
-            #process = subprocess.Popen([python_path,"-m","streamlit",tmp.name], env=environmental_variables)
-            #streamlit_path = subprocess.check_output("which streamlit", shell=True).strip().decode('utf-8')
             streamlit_path = shutil.which("python")
             process = subprocess.Popen([streamlit_path,"run",tmp.name], env=environmental_variables)
             pid = process.pid
@@ -140,14 +129,9 @@ def run_code_v2(self,code):
         with open(filepath,"w") as tmp:
             tmp.write(code)
             tmp.flush()
-        command = f"OPENAI_API_KEY={self.openai_api_key} streamlit run "+filepath
         environmental_variables = {'OPENAI_API_KEY':self.openai_api_key,"STREAMLIT_SERVER_PORT":"8502"}
-        #python_path = subprocess.check_output("which python", shell=True).strip().decode('utf-8')
-        #process = subprocess.Popen([python_path,"-m","streamlit",filepath], env=environmental_variables)
-        #streamlit_path = subprocess.check_output("which streamlit", shell=True).strip().decode('utf-8')
         streamlit_path = shutil.which("streamlit")
         process = subprocess.Popen([streamlit_path,"run",filepath], env=environmental_variables)
-
         pid = process.pid
         return pid