Update app.py

app.py

@@ -1,98 +1,117 @@
+# Save as `interview_copilot.py`
 import streamlit as st
-from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
-import cv2
-import numpy as np
-import requests
-from PIL import Image
-import io
-
-# OCR API Key
-OCR_API_KEY = "K81127294888957"  # Replace with your API key
-
-# Function to use OCR.space API for text extraction
-def ocr_space_image(image):
-    """OCR.space API to extract text from an image"""
-    img_byte_arr = io.BytesIO()
-    image.save(img_byte_arr, format='PNG')
-    img_byte_arr.seek(0)
-
-    url = "https://api.ocr.space/parse/image"
-    payload = {
-        'apikey': OCR_API_KEY,
-        'language': 'eng',
-        'filetype': 'PNG'
+from streamlit_webrtc import webrtc_streamer
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer
+import openai
+import whisper
+import time
+import random
+
+# Configuration for Streamlit
+st.set_page_config(page_title="Interview Copilot", layout="wide")
+st.markdown(
+    """
+    <style>
+    @import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600;700&display=swap');
+    html, body, [class*="css"]  {
+        font-family: 'Poppins', sans-serif;
     }
-    files = {
-        'file': ('image.png', img_byte_arr, 'image/png')
-    }
-
-    response = requests.post(url, data=payload, files=files)
-    result = response.json()
-
-    if response.status_code == 200 and result["OCRExitCode"] == 1:
-        return result["ParsedResults"][0]["ParsedText"]
-    else:
-        error_message = result.get("ErrorMessage", ["Unknown error"])
-        return "OCR Error: " + ', '.join(map(str, error_message))
-
-# Streamlit UI
-st.title("Quiz Checker App")
-st.header("Upload an Answer Key and Start Webcam for Real-Time Evaluation")
-
-# Upload answer key
-uploaded_file = st.file_uploader("Upload Answer Key Image (JPG/PNG)", type=["jpg", "jpeg", "png"])
-
-if uploaded_file:
-    # Process the answer key image
-    answer_key_img = Image.open(uploaded_file).convert("RGB")
-    answer_key_text = ocr_space_image(answer_key_img)
-
-    if "OCR Error" in answer_key_text:
-        st.error("Error extracting text from answer key: " + answer_key_text)
+    </style>
+    """, unsafe_allow_html=True)
+
+# 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")
+job_title = st.text_input("Enter Job Title:", placeholder="e.g., Data Scientist, Software Engineer")
+job_description = st.text_area("Enter Job Description:", placeholder="Provide details about the role")
+
+# Step 2: Upload CV
+st.subheader("Step 2: Upload Your CV")
+uploaded_cv = st.file_uploader("Upload Your CV (PDF or Text):", type=["pdf", "txt"])
+if uploaded_cv:
+    cv_content = uploaded_cv.read().decode("utf-8") if uploaded_cv.type == "text/plain" else "Uploaded PDF file"
+
+# Step 3: Start Interview
+st.subheader("Step 3: Start the Interview")
+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:
-        answers = [ans.strip() for ans in answer_key_text.splitlines() if ans.strip()]
-        st.success("Answer key loaded successfully!")
-        st.write("Extracted Answers:")
-        st.write(answers)
-
-        # Define VideoTransformer class for real-time evaluation
-        class QuizChecker(VideoTransformerBase):
-            def __init__(self):
-                self.answers = answers
-
-            def transform(self, frame):
-                frame = frame.to_ndarray(format="bgr24")
-                gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-
-                # Convert frame to PIL image for OCR
-                pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
-
-                # Extract text from the frame
-                student_text = ocr_space_image(pil_image)
-                if "OCR Error" in student_text:
-                    cv2.putText(frame, "Error in OCR", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-                    return frame
-
-                student_answers = [ans.strip() for ans in student_text.splitlines() if ans.strip()]
-
-                # Compare student answers with the answer key
-                correct_count = 0
-                for idx, student_answer in enumerate(student_answers):
-                    if idx < len(self.answers) and student_answer == self.answers[idx]:
-                        color = (0, 255, 0)  # Green for correct
-                        correct_count += 1
-                    else:
-                        color = (0, 0, 255)  # Red for incorrect
-                    cv2.putText(frame, f"Q{idx+1}: {student_answer}", (10, 50 + idx * 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
-
-                # Display the score on the frame
-                cv2.putText(frame, f"Score: {correct_count}/{len(self.answers)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
-
-                return frame
-
-        # Start webcam with real-time evaluation
-        st.header("Real-Time Quiz Evaluation")
-        webrtc_streamer(key="quiz-checker", video_transformer_factory=QuizChecker, media_stream_constraints={"video": True})
-
-else:
-    st.info("Please upload an answer key image to proceed.")
+        # 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!")
+
+        # 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
+        questions = [
+            "Introduce yourself.",
+            "Why do you want this job?",
+        ]
+        technical_questions = [
+            "Tell me about a challenging project you've worked on.",
+            f"What skills do you bring to the role of {job_title}?",
+        ]
+        non_technical_questions = [
+            "How do you handle conflict in a team setting?",
+            "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
+
+        # Conduct interview
+        responses = []
+        for idx, question in enumerate(all_questions):
+            st.write(f"**Question {idx + 1}: {question}**")
+            user_response = st.text_area(f"Your Answer to Question {idx + 1}", key=f"response_{idx}")
+            responses.append(user_response)
+
+            if time.time() - start_time > duration:
+                st.warning("⏱️ Interview time is up!")
+                break
+
+        # Step 4: Evaluation
+        st.subheader("📊 Evaluation")
+        st.write("Evaluating your performance based on your answers...")
+
+        # 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")
+
+        # 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!")
+        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")