hope last commit

app.py

@@ -18,5 +18,5 @@ def main():
         st.text_area("Bot Response:", value=response_gpt, height=100)
 
 if __name__ == "__main__":
+    imp()
     main()
-

resource.py

@@ -4,39 +4,49 @@ from langchain.llms import GPT4All
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 
 
-try :
+template = """
+You are a friendly chatbot assistant you are going to evaluate a Github Repo i analysed all the repos and made a report
+i'm gonna give a report you just refer the below table
 
-	hf_hub_download(repo_id="dnato/ggml-gpt4all-j-v1.3-groovy.bin", filename="ggml-gpt4all-j-v1.3-groovy.bin", local_dir=".")
+Table:
 
-	local_path=os.getcwd() + "/ggml-gpt4all-j-v1.3-groovy.bin"
+score       Rank      Risk
+1 - 5        A        Low - Simple block
+6 - 10       B        Low - Well structured and stable block
+11 - 20      C        Moderate - Slightly complex block
+21 - 30      D        More than moderate - More complex block
+31 - 40      E        High - Complex block, alarming
+41+          F        Very high - Error-prone, unstable block
 
-	llm = GPT4All(
-	    model=local_path,
-	    callbacks=[StreamingStdOutCallbackHandler()]
-	)
+based on the score and rank you have to return only one most complex repo from report 
 
+Report: {report}
 
+Answer:"""
 
-	llm_chain = LLMChain(prompt=prompt, llm=llm)
 
-except Exception as e:
-	print("Error Loading Model Please Contact Admin",e)
+prompt = PromptTemplate(template=template, input_variables=["report"])
 
 
+def imp() :
 
+	try :
 
+		hf_hub_download(repo_id="dnato/ggml-gpt4all-j-v1.3-groovy.bin", filename="ggml-gpt4all-j-v1.3-groovy.bin", local_dir=".")
 
-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.
+		local_path=os.getcwd() + "/ggml-gpt4all-j-v1.3-groovy.bin"
+
+		llm = GPT4All(
+		    model=local_path,
+		    callbacks=[StreamingStdOutCallbackHandler()]
+		)
 
-Question: {question}
 
-Answer:"""
 
+		llm_chain = LLMChain(prompt=prompt, llm=llm)
 
-prompt = PromptTemplate(template=template, input_variables=["question"])
+	except Exception as e:
+		print("Error Loading Model Please Contact Admin",e)