hope commit

app.py

@@ -1,53 +1,29 @@
-from langchain import PromptTemplate, LLMChain
-from gpt4all import GPT4All
-from huggingface_hub import hf_hub_download
 import streamlit as st
-import shlex as sx
-import os
-import subprocess as sp
-#gpt=GPT4All("ggml-gpt4all-j-v1.3-groovy")
-#hf_hub_download(repo_id="dnato/ggml-gpt4all-j-v1.3-groovy.bin", filename="ggml-gpt4all-j-v1.3-groovy.bin", local_dir=".")
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-template = """
-You are a friendly chatbot assistant that responds in a conversational
-manner to users questions. Keep the answers short, unless specifically
-asked by the user to elaborate on something.
+from resource import *
+from proper_main import *
 
-Question: {question}
 
-Answer:"""
 
-local_path=os.getcwd() + "/ggml-gpt4all-j-v1.3-groovy.bin"
-
-prompt = PromptTemplate(template=template, input_variables=["question"])
-from langchain.llms import GPT4All
-#llm = GPT4All(
-#    model=local_path,
-#    callbacks=[StreamingStdOutCallbackHandler()]
-#)
-
-#llm_chain = LLMChain(prompt=prompt, llm=llm)
-os.chdir("/tmp")
-cm=sx.split("git clone https://github.com/theedamn/Basic-Encrypter-using-Python.git")
-sp.run(cm)
 
 
 def main():
     st.title("GPT4All Chatbot")
 
     # User input
-    query = st.text_input("Enter your message:")
-    cm=sx.split(query)
+    user_url = st.text_input("Enter the Github URL")
+    
 
     # Generate response
     if st.button("Submit"):
-        #response=llm_chain(query)
-        #response = gptj.chat_completion(messages)
-        #answer = response['choices'][0]['message']['content']
-
+	web_scrape(user_url)
+	curr_path=data_cloning()
+	cm=sx.split(query)
+	report=analyse()
+        response_gpt=llm_chain(report)
         # Display the response
-        st.text_area("Bot Response:", value=sp.check_output(cm), height=100)
+        st.text_area("Bot Response:", value=response_gpt, height=100)
 
 if __name__ == "__main__":
-    main()
+	main()
+    
 

proper_main.py

@@ -0,0 +1,122 @@
+try:
+    import requests
+    import os
+    import subprocess as sp
+    from bs4 import BeautifulSoup
+    from nbconvert import PythonExporter
+    import shutil
+
+except Exception as e:
+    print("Some modules are missing:", e)
+    print("Do you want to install them via this Python program?")
+    option = input("Y or N: ")
+    if option.lower() not in ["y", "n"]:
+        exit()
+    elif option.lower() == "n":
+        exit()
+    elif option.lower() == "y":
+        print("Make sure your internet connection is active; otherwise, it may throw an error. Press 'N' to exit.")
+        curr_dir = os.getcwd()
+        os.system("pip install -r " + curr_dir + "/requirements.txt")
+
+
+repos = []
+
+
+def web_scrape(user_url):
+    base_url = "https://www.github.com"
+    user_url = user_url
+
+    if user_url.endswith("/"):
+        user_url = user_url[:-1]
+
+    try:
+        response = requests.get(user_url + "?tab=repositories")
+    except Exception as e:
+        print("Please provide a valid link:", e)
+        web_scrape()
+
+    if response.status_code != 200:
+        print("Please provide a valid link.")
+        web_scrape()
+
+    make_soup = BeautifulSoup(response.text, 'html.parser')
+    li = make_soup.findAll('div', class_='d-inline-block mb-1')
+    if len(li) == 0:
+    	print("Please Provide the Valid Link")
+    	web_scrape()
+
+    for _, i in enumerate(li):
+        for a in i.findAll('a'):
+            new_url = base_url + a['href']
+            repos.append(new_url)
+
+
+def data_cloning():
+    os.mkdir("/tmp/repos")
+    os.chdir("/tmp/repos")
+    for i in repos:
+        sp.run(["git", "clone", i], stdout=sp.DEVNULL, stderr=sp.DEVNULL)
+
+    return os.getcwd()
+
+
+def data_cleaning(directory):
+    exporter = PythonExporter()
+
+    for root, dirs, files in os.walk(directory, topdown=False):
+        for filename in files:
+            file_path = os.path.join(root, filename)
+
+            if filename.endswith(".ipynb"):
+                output, _ = exporter.from_filename(file_path)
+                with open(os.path.join(root, filename[:-6] + ".py"), "w") as script_file:
+                    script_file.write(output)
+                os.remove(file_path)
+
+            if not (filename.endswith(".py") or filename.endswith(".ipynb")):
+                os.remove(file_path)
+
+        for dir_name in dirs:
+            dir_path = os.path.join(root, dir_name)
+            if not os.listdir(dir_path):
+                os.rmdir(dir_path)
+
+
+def analyse():
+    project_and_grades = {}
+
+    for file in os.listdir(os.getcwd()):
+        print(file)
+        path = os.getcwd() + "/" + file
+        
+        cmd = ["radon", "cc", "--total-average", file]
+        res = sp.check_output(cmd)
+        index = res.decode().find("Average")
+        if index <= 0:
+            grade = "A"
+            score = 1
+        else:
+            grade = res.decode()[index:]
+            score = grade[23:-1]
+            score = score[:3]
+            grade=grade[20]
+            
+
+        project_and_grades["Repo " + file] = "Grade " + grade + " Score " + str(score)
+    #shutil.rmtree('/tmp/repos')
+
+    return project_and_grades
+
+
+
+"""def main():
+	web_scrape()
+	curr_path=data_cloning()
+	data_cleaning(curr_path)
+	report=analyse()
+	print(report)
+	
+if __name__ == main():
+	main()
+"""

requirements.txt

@@ -5,3 +5,5 @@ huggingface
 huggingface_hub
 radon
 requests
+bs4
+nbconvert

resource.py

@@ -0,0 +1,42 @@
+from langchain import PromptTemplate, LLMChain
+from huggingface_hub import hf_hub_download
+from langchain.llms import GPT4All
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
+
+try :
+
+	hf_hub_download(repo_id="dnato/ggml-gpt4all-j-v1.3-groovy.bin", filename="ggml-gpt4all-j-v1.3-groovy.bin", local_dir=".")
+
+	local_path=os.getcwd() + "/ggml-gpt4all-j-v1.3-groovy.bin"
+
+	llm = GPT4All(
+	    model=local_path,
+	    callbacks=[StreamingStdOutCallbackHandler()]
+	)
+
+
+
+	llm_chain = LLMChain(prompt=prompt, llm=llm)
+
+except Exception as e:
+	print("Error Loading Model Please Contact Admin",e)
+
+
+
+
+
+template = """
+You are a friendly chatbot assistant that responds in a conversational
+manner to users questions. Keep the answers short, unless specifically
+asked by the user to elaborate on something.
+
+Question: {question}
+
+Answer:"""
+
+
+prompt = PromptTemplate(template=template, input_variables=["question"])
+
+
+