Update app.py

app.py

@@ -1,14 +1,13 @@
-# Save as `interview_copilot.py`
+# Install dependencies if running on Colab
+# !pip install streamlit streamlit-webrtc openai openai-whisper transformers sentence-transformers streamlit-lottie
+
 import streamlit as st
-from streamlit_webrtc import webrtc_streamer
+from streamlit_webrtc import webrtc_streamer, WebRtcMode, RTCConfiguration
 from transformers import pipeline
-from sentence_transformers import SentenceTransformer
-import openai
-import whisper
 import time
 import random
 
-# Configuration for Streamlit
+# Streamlit Configuration
 st.set_page_config(page_title="Interview Copilot", layout="wide")
 st.markdown(
     """
@@ -23,7 +22,6 @@ st.markdown(
 # Page Title
 st.title("🎙️ Interview Copilot")
 st.write("### Conduct professional mock interviews, receive feedback, and ace your next job opportunity!")
-st.image("https://via.placeholder.com/800x200?text=Interview+Copilot", use_column_width=True)
 
 # Step 1: Input Job Title
 st.subheader("Step 1: Enter Job Details")
@@ -42,17 +40,34 @@ if st.button("Start Interview"):
     if not job_title or not job_description or not uploaded_cv:
         st.error("Please fill in all the required fields (Job Title, Description, CV)!")
     else:
-        # Simulate Interview Start
         st.subheader("🎥 Live Interview Simulation")
         st.write("The interview will begin shortly. Prepare yourself!")
         st.write("👤 **Bot:** Hello, let’s start the interview!")
 
+        # RTC Configuration for WebRTC
+        RTC_CONFIGURATION = RTCConfiguration(
+            {
+                "iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]
+            }
+        )
+
+        # Activate webcam
+        webrtc_streamer(
+            key="interview",
+            mode=WebRtcMode.SENDRECV,
+            rtc_configuration=RTC_CONFIGURATION,
+            media_stream_constraints={
+                "video": True,
+                "audio": True,
+            },
+        )
+
         # Timer
         start_time = time.time()
         duration = 5 * 60  # 5 minutes
-        webrtc_streamer(key="camera", rtc_configuration={"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]})
 
         # Predefined questions
+        st.write("### Interview Questions")
         questions = [
             "Introduce yourself.",
             "Why do you want this job?",
@@ -66,16 +81,9 @@ if st.button("Start Interview"):
             "What are your strengths and weaknesses?",
         ]
 
-        # Generate AI-based questions using Hugging Face models
-        question_generator = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
-        generated_questions = question_generator(
-            f"Generate 3 interview questions for the role of {job_title} based on this CV:\n{cv_content}",
-            max_length=50, num_return_sequences=3
-        )
-        ai_questions = [q["generated_text"] for q in generated_questions]
-
-        # Combine questions
-        all_questions = questions + random.sample(technical_questions, 2) + random.sample(non_technical_questions, 2) + ai_questions
+        # Add random questions to interview
+        random_questions = random.sample(technical_questions, 2) + random.sample(non_technical_questions, 2)
+        all_questions = questions + random_questions
 
         # Conduct interview
         responses = []
@@ -84,34 +92,30 @@ if st.button("Start Interview"):
             user_response = st.text_area(f"Your Answer to Question {idx + 1}", key=f"response_{idx}")
             responses.append(user_response)
 
+            # Check if time exceeded
             if time.time() - start_time > duration:
                 st.warning("⏱️ Interview time is up!")
                 break
 
-        # Step 4: Evaluation
+        # Evaluation
         st.subheader("📊 Evaluation")
-        st.write("Evaluating your performance based on your answers...")
+        st.write("Analyzing your responses...")
 
-        # Communication Skills Analysis
         sentiment_analyzer = pipeline("sentiment-analysis")
-        sentiment_results = [sentiment_analyzer(response)[0] for response in responses if response]
-        positive_count = sum(1 for result in sentiment_results if result["label"] == "POSITIVE")
+        positive_responses = sum(
+            1 for response in responses if response and sentiment_analyzer(response)[0]["label"] == "POSITIVE"
+        )
+        score = (positive_responses / len(all_questions)) * 100
 
-        # Feedback
-        score = positive_count * 10  # Simplified scoring
-        if score >= 70:
-            st.success(f"🎉 Great job! You scored {score}/100. You're ready to ace the interview!")
+        # Display feedback
+        if score > 70:
+            st.success(f"🎉 Excellent! You scored {score:.2f}/100. You're ready for the job!")
         else:
-            st.error(f"🙁 You scored {score}/100. Here's how you can improve:")
-            st.write("- Practice communicating your ideas clearly.")
-            st.write("- Provide detailed examples to technical questions.")
-            st.write("- Work on your confidence and tone of speech.")
-
-        # CV Recommendations
-        st.subheader("📄 CV Recommendations")
-        st.write("- Add measurable achievements to your CV.")
-        st.write("- Tailor your CV to match the job description.")
-
-# Add Graphics and Animation
-st.markdown("---")
-st.markdown("#### Powered by AI | Built with 💻 and Streamlit")
+            st.error(f"🙁 You scored {score:.2f}/100. Here's how you can improve:")
+            st.write("- Practice answering common interview questions.")
+            st.write("- Provide more detailed responses.")
+            st.write("- Speak with confidence and clarity.")
+
+        st.subheader("📄 CV Feedback")
+        st.write("- Add quantifiable achievements to your CV.")
+        st.write("- Tailor your CV to the job description.")