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requirements.txt

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+streamlit
+torch
+transformers
+numpy

streamlitApp.py

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+import streamlit as st
+from transformers import BertTokenizer, BertModel
+import torch
+import numpy as np
+
+# Load the pre-trained BERT model and tokenizer
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = BertModel.from_pretrained('bert-base-uncased')
+
+# Define criteria for scoring
+criteria = {
+    "technical": ["machine learning", "data", "preprocess", "decision tree", "SVM", "neural network", "hyperparameter"],
+    "problem_solving": ["cross-validation", "grid search", "evaluate", "optimize", "performance"],
+    "communication": ["I would", "then", "and", "also"]
+}
+
+# Function to encode a response using BERT
+def encode_response(response):
+    inputs = tokenizer(response, return_tensors='pt', padding=True, truncation=True)
+    outputs = model(**inputs)
+    return outputs.last_hidden_state.mean(dim=1).squeeze().detach().numpy()
+
+# Function to score the response based on predefined criteria
+def score_response(response, criteria):
+    scores = {}
+    for criterion, keywords in criteria.items():
+        scores[criterion] = sum([1 for word in keywords if word in response]) / len(keywords)
+    return scores
+
+# Function to rank candidates by average score
+def rank_candidates(candidates):
+    for candidate in candidates:
+        avg_score = np.mean(list(candidate['scores'].values()))
+        candidate['avg_score'] = avg_score
+    ranked_candidates = sorted(candidates, key=lambda x: x['avg_score'], reverse=True)
+    return ranked_candidates
+
+# Streamlit app
+st.title("AI Role Candidate Screening")
+
+# Input for the number of candidates
+num_candidates = st.number_input("Enter the number of candidates:", min_value=1, max_value=10, value=3)
+
+# Create input fields for candidate names and responses
+mock_interviews = []
+for i in range(num_candidates):
+    name = st.text_input(f"Enter the name of Candidate {i+1}:", key=f"name_{i}")
+    response = st.text_area(f"Enter the interview response for {name}:", key=f"response_{i}")
+    if name and response:
+        mock_interviews.append({"name": name, "response": response})
+
+# Analyze the candidates when the user clicks the "Analyze" button
+if st.button('Analyze Responses'):
+    if mock_interviews:
+        # Encode and score each candidate
+        scored_candidates = []
+        for candidate in mock_interviews:
+            scores = score_response(candidate['response'], criteria)
+            candidate['scores'] = scores
+            candidate['encoded'] = encode_response(candidate['response'])
+            scored_candidates.append(candidate)
+        
+        # Rank the candidates based on scores
+        ranked_candidates = rank_candidates(scored_candidates)
+        
+        # Display the results
+        st.write("### Candidate Rankings")
+        for rank, candidate in enumerate(ranked_candidates, 1):
+            st.write(f"**Rank {rank}: {candidate['name']}**")
+            st.write(f"Average Score: {candidate['avg_score']:.2f}")
+            st.write(f"Scores: {candidate['scores']}")
+    else:
+        st.write("Please enter candidate responses.")