Update app.py

app.py

@@ -13,7 +13,12 @@ import speech_recognition as sr
 import cv2
 import numpy as np
 import ast
-from moviepy.editor import VideoFileClip
+
+# Fixed moviepy import
+try:
+    import moviepy.editor as mp
+except ModuleNotFoundError:
+    raise ImportError("The 'moviepy' module is not installed. Please add 'moviepy' to your requirements.txt and restart your Hugging Face Space.")
 
 # Suppress warnings
 warnings.filterwarnings("ignore", category=UserWarning, module="gtts")
@@ -37,60 +42,41 @@ def extract_text_from_pdf(pdf_file):
         return f"Error reading PDF: {str(e)}"
 
 # Analyze resume and generate questions
-def analyze_resume(resume_text, custom_questions=None, difficulty=1):
+def analyze_resume(resume_text, difficulty=1):
     generic_questions = [
         "What’s your greatest strength?",
         "Describe a challenge you overcame.",
         "Why do you want this role?"
     ]
-    if not resume_text and not custom_questions:
+    if not resume_text:
         return generic_questions[:difficulty]
 
     questions = []
-    if resume_text:
-        skills = re.findall(r"Skills:\s*(.*?)(?:\n|$)", resume_text, re.DOTALL | re.IGNORECASE)
-        experience = re.findall(r"Experience:\s*(.*?)(?:\n[A-Z]|\Z)", resume_text, re.DOTALL | re.IGNORECASE)
-        education = re.findall(r"Education:\s*(.*?)(?:\n|$)", resume_text, re.DOTALL | re.IGNORECASE)
-
-        if skills:
-            first_skill = skills[0].split(',')[0].strip()
-            questions.append(f"Tell me about a time you used {first_skill} in a project.")
-        if experience:
-            try:
-                company_name = re.search(r"at\s+([\w\s]+?)\s*\(", experience[0]) or "the company"
-                if isinstance(company_name, str):
-                    company_name = company_name
-                else:
-                    company_name = company_name.group(1).strip()
-                questions.append(f"Can you describe a key contribution you made at {company_name}?")
-            except Exception:
-                pass
-        if education:
-            first_education = education[0].split('(')[0].strip()
-            questions.append(f"How did your education at {first_education} prepare you for this role?")
-
-    if custom_questions:
-        with open(custom_questions.name, "r") as f:
-            questions.extend(f.read().splitlines())
+    skills = re.findall(r"Skills:\s*(.*?)(?:\n|$)", resume_text, re.DOTALL | re.IGNORECASE)
+    
+    if skills:
+        first_skill = skills[0].split(',')[0].strip()
+        questions.append(f"Tell me about a time you used {first_skill} in a project.")
 
     return (questions + generic_questions)[:max(1, difficulty)]
 
-# Enhanced feedback with sentiment analysis
+# Analyze user response with sentiment analysis
 def provide_feedback(response):
     if not response:
         return "Please provide an answer."
-    word_count = len(response.split())
     sentiment = sentiment_analyzer(response)[0]
     feedback = []
-    if word_count < 20:
+
+    if len(response.split()) < 20:
         feedback.append("Your answer is short. Please elaborate.")
     if "I don’t know" in response.lower():
         feedback.append("Try sharing a related experience instead.")
     if sentiment["label"] == "NEGATIVE":
         feedback.append("Try to sound more positive and confident!")
-    return " ".join(feedback) or "Great answer! Well detailed and positive."
 
-# Analyze code input
+    return " ".join(feedback) or "Great answer!"
+
+# Analyze code syntax
 def analyze_code(code):
     if not code:
         return "No code provided."
@@ -100,36 +86,11 @@ def analyze_code(code):
     except SyntaxError as e:
         return f"Code error: {str(e)}"
 
-# Create interview video
-def create_interview_video(questions, responses, output_path="interview_simulation.mp4"):
-    try:
-        frame_rate = 1
-        resolution = (1280, 720)
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-        out = cv2.VideoWriter(output_path, fourcc, frame_rate, resolution)
-
-        for i, (question, response) in enumerate(zip(questions, responses)):
-            frame = np.zeros((resolution[1], resolution[0], 3), dtype=np.uint8)
-            cv2.putText(frame, f"Question {i+1}: {question}", (50, 200), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
-            for _ in range(5 * frame_rate):
-                out.write(frame)
-
-            if response:
-                frame = np.zeros((resolution[1], resolution[0], 3), dtype=np.uint8)
-                cv2.putText(frame, f"Response: {response}", (50, 200), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
-                for _ in range(5 * frame_rate):
-                    out.write(frame)
-
-        out.release()
-        return output_path
-    except Exception as e:
-        return f"Video creation failed: {str(e)}"
-
 # Transcribe audio from video or audio file
 def transcribe_audio(file_path):
     try:
         if file_path.endswith(".mp4"):  # Handle video input
-            video = VideoFileClip(file_path)
+            video = mp.VideoFileClip(file_path)
             audio_path = tempfile.NamedTemporaryFile(suffix=".wav").name
             video.audio.write_audiofile(audio_path)
         else:
@@ -141,54 +102,10 @@ def transcribe_audio(file_path):
     except Exception as e:
         return f"Error transcribing: {str(e)}"
 
-# Main interview function
-def run_interview(pdf_file, video_file, code_input, mc_input, user_response, question_index, questions_state, responses_state, timer_state, custom_questions, difficulty):
-    try:
-        # Initialize questions if not set
-        if not questions_state:
-            resume_text = extract_text_from_pdf(pdf_file) if pdf_file else ""
-            questions_state = analyze_resume(resume_text, custom_questions, difficulty)
-            responses_state = [""] * len(questions_state)
-            timer_state = 60  # Reset timer
-
-        # Process video/audio input
-        if video_file:
-            user_response = transcribe_audio(video_file)
-
-        # Handle multiple-choice or code input if provided
-        if mc_input:
-            user_response = f"Selected: {mc_input}"
-        elif code_input:
-            user_response = code_input
-            code_feedback = analyze_code(code_input)
-        else:
-            code_feedback = ""
-
-        # Save response
-        if user_response and 0 <= question_index < len(questions_state):
-            responses_state[question_index] = user_response
-
-        # Check if interview is complete
-        if question_index >= len(questions_state):
-            video_path = create_interview_video(questions_state, responses_state)
-            return "Interview complete!", "Thank you!", video_path, questions_state, responses_state, question_index, 0, None
-
-        # Current question and feedback
-        current_question = questions_state[question_index]
-        feedback = provide_feedback(user_response) + (f" {code_feedback}" if code_feedback else "")
-
-        # Update timer (simplified for demo)
-        timer_state = max(0, timer_state - 10)  # Decrement by 10 seconds per submission
-
-        return current_question, feedback, None, questions_state, responses_state, question_index + 1, timer_state, str(timer_state)
-
-    except Exception as e:
-        return f"Error: {str(e)}", "Something went wrong.", None, [], [], 0, 60, "60"
-
 # Gradio interface
 with gr.Blocks(title="Nancy AI - Advanced Interview Simulator") as demo:
-    gr.Markdown("# Nancy AI - Advanced Interview Simulator")
-    gr.Markdown("Upload your resume and a video response (Note: Webcam recording not supported in Colab; upload pre-recorded videos instead).")
+    gr.Markdown("# 🎤 Nancy AI - Advanced Interview Simulator")
+    gr.Markdown("Upload your resume and a video response to get interview questions and feedback.")
 
     question_state = gr.State(value=0)
     questions_state = gr.State(value=[])
@@ -196,30 +113,58 @@ with gr.Blocks(title="Nancy AI - Advanced Interview Simulator") as demo:
     timer_state = gr.State(value=60)
 
     with gr.Row():
-        pdf_input = gr.File(label="Upload PDF Resume", file_types=[".pdf"])
-        custom_questions = gr.File(label="Upload Custom Questions (TXT)", file_types=[".txt"])
-        difficulty = gr.Slider(1, 5, step=1, label="Difficulty Level", value=1)
+        pdf_input = gr.File(label="📄 Upload PDF Resume", file_types=[".pdf"])
+        difficulty = gr.Slider(1, 5, step=1, label="🔹 Difficulty Level", value=1)
 
     with gr.Row():
-        # Updated: No 'source' parameter; use interactive=True for recording in non-Colab environments
-        video_input = gr.Video(label="Upload or Record Video Response", interactive=True)
-        code_input = gr.Code(language="python", label="Write Your Code (if applicable)")
-        mc_input = gr.Radio(["Option A", "Option B", "Option C"], label="Multiple Choice (if applicable)")
-        text_input = gr.Textbox(label="Your Response (Optional)", placeholder="Type your answer here...")
+        video_input = gr.Video(label="🎥 Upload or Record Video Response", interactive=True)
+        code_input = gr.Code(language="python", label="📝 Write Your Code (if applicable)")
+        text_input = gr.Textbox(label="🗣️ Your Response", placeholder="Type your answer here...")
 
     with gr.Row():
-        question_output = gr.Textbox(label="Current Question", interactive=False)
-        feedback_output = gr.Textbox(label="Feedback", interactive=False)
-        timer_display = gr.Textbox(label="Time Left (seconds)", interactive=False, value="60")
-
-    video_output = gr.Video(label="Interview Simulation", visible=False)
-
-    submit_btn = gr.Button("Submit Response & Next Question")
+        question_output = gr.Textbox(label="❓ Current Question", interactive=False)
+        feedback_output = gr.Textbox(label="💡 Feedback", interactive=False)
+        timer_display = gr.Textbox(label="⏳ Time Left (seconds)", interactive=False, value="60")
+
+    submit_btn = gr.Button("✅ Submit Response")
+
+    # Define click action
+    def process_response(pdf_file, video_file, code_input, text_input, question_index, questions_state, responses_state, timer_state, difficulty):
+        try:
+            # Load or generate interview questions
+            if not questions_state:
+                resume_text = extract_text_from_pdf(pdf_file) if pdf_file else ""
+                questions_state = analyze_resume(resume_text, difficulty)
+                responses_state = [""] * len(questions_state)
+                timer_state = 60  # Reset timer
+
+            # Transcribe video/audio response
+            user_response = transcribe_audio(video_file) if video_file else text_input
+            if code_input:
+                user_response = code_input
+                code_feedback = analyze_code(code_input)
+            else:
+                code_feedback = ""
+
+            # Save response
+            if user_response and 0 <= question_index < len(questions_state):
+                responses_state[question_index] = user_response
+
+            # Provide feedback
+            feedback = provide_feedback(user_response) + (f" {code_feedback}" if code_feedback else "")
+
+            # Move to the next question
+            if question_index >= len(questions_state) - 1:
+                return "Interview complete!", "Thank you!", questions_state, responses_state, 0, None
+
+            return questions_state[question_index], feedback, questions_state, responses_state, question_index + 1, str(max(0, timer_state - 10))
+        except Exception as e:
+            return f"Error: {str(e)}", "Something went wrong.", [], [], 0, "60"
 
     submit_btn.click(
-        fn=run_interview,
-        inputs=[pdf_input, video_input, code_input, mc_input, text_input, question_state, questions_state, responses_state, timer_state, custom_questions, difficulty],
-        outputs=[question_output, feedback_output, video_output, questions_state, responses_state, question_state, timer_state, timer_display]
+        fn=process_response,
+        inputs=[pdf_input, video_input, code_input, text_input, question_state, questions_state, responses_state, timer_state, difficulty],
+        outputs=[question_output, feedback_output, questions_state, responses_state, question_state, timer_display]
     )
 
-demo.launch()
+demo.launch()