Task 4: Connect Frontend with the Backend

app.py

@@ -1,12 +1,71 @@
 #make a stremlit aplication to import a pdf file and convert it to text and display text in another output window and an option to download file generated
 import streamlit as st
 import os
-
+import multiprocessing
+from functools import wraps
 from langchain.llms import OpenAI
 from langchain.document_loaders import TextLoader
 from langchain.document_loaders import PyPDFLoader
 from langchain.prompts import PromptTemplate
 
+import urllib.request
+def connect(host='http://google.com'):
+    try:
+        urllib.request.urlopen(host) #Python 3.x
+        return True
+    except:
+        return False
+
+
+
+def parametrized(dec):
+    def layer(*args, **kwargs):
+        def repl(f):
+            return dec(f, *args, **kwargs)
+        return repl
+    return layer
+def function_runner(*args, **kwargs):
+    """Used as a wrapper function to handle
+    returning results on the multiprocessing side"""
+
+    send_end = kwargs.pop("__send_end")
+    function = kwargs.pop("__function")
+    try:
+        result = function(*args, **kwargs)
+    except Exception as e:
+        send_end.send(e)
+        return
+    send_end.send(result)
+
+@parametrized
+def run_with_timer(func, max_execution_time):
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        recv_end, send_end = multiprocessing.Pipe(False)
+        kwargs["__send_end"] = send_end
+        kwargs["__function"] = func
+
+        ## PART 2
+        p = multiprocessing.Process(target=function_runner, args=args, kwargs=kwargs)
+        p.start()
+        p.join(max_execution_time)
+        if p.is_alive():
+            p.terminate()
+            p.join()
+            #raise TimeExceededException("OpenAI taking too long to respond")
+            st.error("OpenAI taking too long to respond. Refresh and Restart again.")
+        result = recv_end.recv()
+
+        if isinstance(result, Exception):
+            #raise result
+            st.error(result)
+
+
+        return result
+
+    return wrapper
+
+@run_with_timer(max_execution_time=20)
 def op_readme():
     llm = OpenAI()
 
@@ -26,12 +85,13 @@ def op_readme():
     )
 
     from langchain.chains import LLMChain
+
     chain = LLMChain(llm=llm, prompt=prompt)
-
     # Run the chain only specifying the input variable.
     j=0
-
-    os.remove("result.md")
+    file="result.md"
+    if os.path.exists(file):
+        os.remove("result.md")
 
     with open('result.md', 'w') as f:
         for i in range(0,len(full_content),2000):
@@ -43,18 +103,26 @@ def op_readme():
 
     return "Success"
 
+
+
+#signal.signal(signal.SIGALRM, timeout_handler)
 st.title("Slides Summarizer \n(PDF to Markdown)")
 os.environ["OPENAI_API_KEY"] = st.text_input("Enter your OpenAI API Key")
 
-
 file = st.file_uploader("Upload Your Slides", type="pdf")
 if file: 
     if os.environ["OPENAI_API_KEY"] == "":
-        st.write("Please enter your API key")
+        #print(openai.api_key )
+        st.error("Please enter your API key")
     else:
         with open("slides.pdf", "wb") as f:
             f.write(file.getbuffer())
         st.success("Saved File Successfully")
+        if(not connect()):
+            st.error("No Internet!")
+        while(not connect()):
+            pass
+        st.success("Connected to Internet Successfully")
         pdf = op_readme()
         if pdf=="Success":
             st.write("File converted successfully. Download the file to view the summary.")
@@ -66,3 +134,6 @@ def op_readme():
                     mime='text/markdown'
                 )
 
+
+
+

requirements.txt

@@ -4,3 +4,6 @@ pandas
 requests
 paytmchecksum
 langchain
+openai
+requests
+urllib