Create bertimproved.py

bertimproved.py

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+import streamlit as st
+from transformers import BertForQuestionAnswering, BertTokenizer
+import torch
+from io import BytesIO
+import PyPDF2
+import pandas as pd
+
+# Initialize session state to store the log of QA pairs and satisfaction responses
+if 'qa_log' not in st.session_state:
+    st.session_state.qa_log = []
+
+def extract_text_from_pdf(pdf_file):
+    pdf_reader = PyPDF2.PdfReader(BytesIO(pdf_file.read()))
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+def answer_question(question, context, model, tokenizer):
+    inputs = tokenizer.encode_plus(
+        question,
+        context,
+        add_special_tokens=True,
+        return_tensors="pt",
+        truncation="only_second",
+        max_length=512,
+    )
+    outputs = model(**inputs, return_dict=True)
+    answer_start_scores = outputs.start_logits
+    answer_end_scores = outputs.end_logits
+    answer_start = torch.argmax(answer_start_scores)
+    answer_end = torch.argmax(answer_end_scores) + 1
+    input_ids = inputs["input_ids"].tolist()[0]
+    answer = tokenizer.convert_tokens_to_string(
+        tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])
+    )
+    return answer
+
+st.title("Resume Question Answering")
+
+uploaded_file = st.file_uploader("Upload your resume (PDF format only)", type=["pdf"])
+
+if uploaded_file is not None:
+    resume_text = extract_text_from_pdf(uploaded_file)
+    st.write("Resume Text:")
+    st.write(resume_text)
+
+    user_question = st.text_input("Ask a question based on your resume:")
+
+    if user_question:
+        model = BertForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+        tokenizer = BertTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
+
+        answer = answer_question(user_question, resume_text, model, tokenizer)
+        st.write("Answer:")
+        st.write(answer)
+
+        # Ask for user feedback on satisfaction
+        satisfaction = st.radio('Are you satisfied with the answer?', ('Yes', 'No'), key='satisfaction')
+        
+        # Log the interaction
+        st.session_state.qa_log.append({
+            'Question': user_question,
+            'Answer': answer,
+            'Satisfaction': satisfaction
+        })
+
+        # Display the log in a table format
+        st.write("Interaction Log:")
+        log_df = pd.DataFrame(st.session_state.qa_log)
+        st.dataframe(log_df)