Update app.py

app.py

@@ -1,6 +1,84 @@
 import gradio as gr
 import whisper
 from transformers import pipeline
+import gradio as gr
+import pandas as pd
+from io import StringIO
+import os,re
+from langchain.llms import OpenAI
+import pandas as pd
+
+from langchain.document_loaders import UnstructuredPDFLoader
+
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.llms import OpenAI
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import TextLoader
+from langchain.prompts import PromptTemplate
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+
+def predict(file_obj):
+    loader = UnstructuredPDFLoader(file_obj.orig_name)
+    data = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    texts = text_splitter.split_documents(data)    
+  
+    embeddings = OpenAIEmbeddings()
+    docsearch = Chroma.from_documents(texts, embeddings)
+    qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="map_reduce", retriever=docsearch.as_retriever())
+    
+    prompt_template = """Ignore all previous instructions. You are the world's best interviewer now. I am going to give you a resume of a candidate. Analyze the resume in 4 categories: Education, Work Experience, Projects and Internships, Others including interests, skills etc. Be simple, direct and commanding. Start with greeting the candidate with a 2 line relatable introduction emphasizing your superiority. Ask the candidate if they have a particular company and a role that they want to apply for. 
+If the candidate mentions either the company or the role, then ensure all questions that would be asked will are related to it.
+If they don't mention either the company or role clearly, then ignore this and move to the next step. 
+Then, give a one line response acknowledging the candidate or if they are not clear about the company or the role then acknowledge positively that you would ask practice interview questions. Then ask the candidate topic would they like to start with. There are 4 categories of questions: educational background related, role related or technical questions, behavioral questions and HR or culture related questions. Here, the candidate will have to put an input. 
+Now you will have to ask interview questions. Ensure the questions are good have test the candidate's knowledge. You can choose between longer case based questions, hypothetical questions or academic questions etc. as you deem fit. 
+If the candidate types educational background related, ask it 3-4 most relevant questions related to their education based on their resume which are relevant for the role or the company. 
+If the candidate types role related or technical related then ask accordingly. Here you can even ask a coding question or test their technical understanding etc. 
+Similarly, do it for behavioral questions and HR or culture related questions. You can also be creative, funny, or show emotions at time.
+{context}
+Question: {question}
+Answer in possible questions for interview:"""
+    PROMPT = PromptTemplate(
+        template=prompt_template, input_variables=["context", "question"]
+    )
+    chain_type_kwargs = {"prompt": PROMPT}
+    qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)
+    response = []
+    category = ["Technical", "Education Background", "Behaviour", "Project Specific"]
+    for value in category:
+
+        response.append({value:ai(qa, value)})
+
+    html_output = ""
+    for obj in response:
+    # Loop through the key-value pairs in the object
+        for key, value in obj.items():
+            value = re.sub(r'[\d\.]+', '', value)
+            value_list = value.strip().split('\n')
+            value_html = "<ol>"
+            for item in value_list:
+                value_html += "<li>{}</li>".format(item.strip())
+            value_html += "</ol>"
+            html_output += "<h2>{}</h2>".format(key)
+            html_output += value_html
+
+    
+    return html_output
+    
+def ai(qa,category):
+    query = "please suggest "+ category +" interview questions"
+    data = list(filter(None, qa.run(query).split('\n')))
+    results = list(filter(lambda x: x != ' ', data))
+    results = "\n".join(results)
+    
+    return results
 
 model = whisper.load_model("base")
 sentiment_analysis = pipeline("sentiment-analysis", framework="pt", model="SamLowe/roberta-base-go_emotions")