Rename streamlitApp.py to app.py

streamlitApp.py → app.py

@@ -1,73 +1,103 @@
-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.")
+import streamlit as st
+import whisper
+from moviepy.editor import VideoFileClip
+from tempfile import NamedTemporaryFile
+import numpy as np
+from transformers import BertTokenizer, BertModel
+import torch
+import os
+
+# Load the pre-trained BERT model and tokenizer
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = BertModel.from_pretrained('bert-base-uncased')
+
+# Load Whisper model for transcription
+whisper_model = whisper.load_model("base")
+
+# Define criteria for scoring responses
+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.lower()]) / 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
+
+# Function to extract audio from the video and perform transcription using Whisper
+def transcribe_video(video_file):
+    # Save the uploaded file to a temporary location
+    with NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video_file:
+        temp_video_file.write(video_file.read())  # Write video file to the temp file
+        temp_video_path = temp_video_file.name
+
+    # Load the video and extract audio
+    video = VideoFileClip(temp_video_path)
+    audio_file = "audio.wav"
+    video.audio.write_audiofile(audio_file)
+    
+    # Perform transcription with Whisper
+    whisper_model = whisper.load_model("base")  # Ensure model is loaded here
+    transcription = whisper_model.transcribe(audio_file)
+
+    # Clean up temporary files
+    os.remove(audio_file)
+    os.remove(temp_video_path)
+
+    return transcription['text']
+
+# Streamlit app
+st.title("AI Role Candidate Screening via Video Interview")
+
+# Input for the number of candidates
+num_candidates = st.number_input("Enter the number of candidates:", min_value=1, max_value=10, value=1)
+
+mock_interviews = []
+for i in range(num_candidates):
+    video_file = st.file_uploader(f"Upload interview video for Candidate {i+1}:", type=["mp4", "mov", "avi"], key=f"video_{i}")
+    if video_file:
+        st.write(f"Processing video for Candidate {i+1}...")
+        transcription = transcribe_video(video_file)
+        st.write(f"Transcript for Candidate {i+1}: {transcription}")
+        mock_interviews.append({"name": f"Candidate {i+1}", "response": transcription})
+
+# 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 upload videos for all candidates.")