Update app.py

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import os
-import re
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import FAISS
@@ -9,55 +8,56 @@ from groq import Groq
 from dotenv import load_dotenv
 from faster_whisper import WhisperModel
 from elevenlabs.client import ElevenLabs
-from gtts import gTTS
+from elevenlabs import play
 import tempfile
 
-# Load environment variables from .env
+# Load environment variables
 load_dotenv()
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-ELEVENLABS_API_KEY = os.getenv("ELEVENLABS_API_KEY")
 
-# Check API keys
-if not GROQ_API_KEY or not ELEVENLABS_API_KEY:
-    raise EnvironmentError("Missing API keys. Please create a .env file with GROQ_API_KEY and ELEVENLABS_API_KEY.")
+# Initialize APIs
+GROQ_API_KEY = "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"
+ELEVENLABS_API_KEY = "ap2_69e1e821-6ea7-4fa0-88dc-ba54f2ac246c"
 
 # Initialize clients
 groq_client = Groq(api_key=GROQ_API_KEY)
 elevenlabs_client = ElevenLabs(api_key=ELEVENLABS_API_KEY)
-whisper_model = WhisperModel("small", device="cpu", compute_type="int8")
 
-def clean_markdown(text):
-    return re.sub(r'[*_#`]+', '', text)
+# Initialize Whisper model
+whisper_model = WhisperModel("small", device="cpu", compute_type="int8")
 
 def summarize_resume(resume_text):
+    """Generate a concise summary of key resume points"""
     prompt = f"""Create a concise summary of this resume highlighting:
     1. Professional title/role
     2. Years of experience
     3. Core skills/competencies
     4. Education background
     5. Notable achievements
-
+    
     Resume:
     {resume_text[:3000]}... [truncated]"""
+    
     response = groq_client.chat.completions.create(
         messages=[{"role": "user", "content": prompt}],
         model="llama3-70b-8192",
         temperature=0.3,
     )
-    return clean_markdown(response.choices[0].message.content)
+    return response.choices[0].message.content
 
 def calculate_ats_score(resume_text):
+    """Calculate ATS score based on resume content"""
     prompt = f"""Analyze this resume and calculate an ATS score (0-100) considering:
     1. Keyword optimization (20 pts)
     2. Section organization (20 pts)
     3. Experience quality (20 pts)
     4. Education completeness (20 pts)
     5. Readability (20 pts)
-
+    
     Return ONLY the numerical score and nothing else.
-
+    
     Resume:
     {resume_text[:3000]}... [truncated]"""
+    
     response = groq_client.chat.completions.create(
         messages=[{"role": "user", "content": prompt}],
         model="llama3-70b-8192",
@@ -66,125 +66,159 @@ def calculate_ats_score(resume_text):
     try:
         return int(response.choices[0].message.content.strip())
     except:
-        return 50
+        return 50  # Default if parsing fails
 
 def process_resume(file):
+    """Process uploaded resume PDF"""
     try:
+        # Load and process PDF
         loader = PyPDFLoader(file.name)
         docs = RecursiveCharacterTextSplitter(
             chunk_size=1000,
             chunk_overlap=200,
             separators=["\n\n", "\n", " ", ""]
         ).split_documents(loader.load())
+        
+        # Create vector store
         embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
         FAISS.from_documents(docs, embeddings).save_local("resume_index")
+        
+        # Generate outputs
         full_text = "\n".join([doc.page_content for doc in docs])
         gr.Info("✅ Resume processed successfully!")
         return summarize_resume(full_text), f"ATS Score: {calculate_ats_score(full_text)}/100"
+        
     except Exception as e:
-        gr.Warning(f"❌ Error: {e}")
-        return f"Error: {e}", "ATS Score: N/A"
+        gr.Warning(f"❌ Error: {str(e)}")
+        return f"Error: {str(e)}", "ATS Score: N/A"
 
 def transcribe_audio(audio_path):
-    if not audio_path:
-        return "No audio recorded"
+    """Convert speech to text using Whisper"""
     segments, _ = whisper_model.transcribe(audio_path)
     return " ".join([segment.text for segment in segments])
 
 def generate_question(resume_text):
+    """Generate general interview questions based on resume"""
     prompt = f"""Generate one general interview question focusing on:
     - Teamwork experiences
     - Challenges overcome
     - Learning experiences
     - Career motivations
     - Problem-solving examples
-
+    
     Make it conversational and open-ended.
-
+    
     Resume Excerpt:
     {resume_text[:2000]}... [truncated]"""
+    
     response = groq_client.chat.completions.create(
         messages=[{"role": "user", "content": prompt}],
         model="llama3-70b-8192",
         temperature=0.7,
     )
-    return clean_markdown(response.choices[0].message.content)
+    return response.choices[0].message.content
 
 def evaluate_response(question, response_text):
+    """Evaluate interview response"""
     prompt = f"""Evaluate this interview response on:
     1. Clarity (1-5)
     2. Confidence (1-5)
     3. Relevance (1-5)
     4. Suggested improvements
-
+    
     Question: {question}
     Response: {response_text}"""
+    
     evaluation = groq_client.chat.completions.create(
         messages=[{"role": "user", "content": prompt}],
         model="llama3-70b-8192",
         temperature=0.2,
     )
-    return clean_markdown(evaluation.choices[0].message.content)
+    return evaluation.choices[0].message.content
 
-def gtts_speak(text):
+def speak_feedback(text):
+    """Convert text feedback to speech"""
     try:
         if not text.strip():
-            raise ValueError("Empty text")
-        tts = gTTS(text, lang="en", tld="com")
+            raise ValueError("Empty feedback text")
+            
+        audio = elevenlabs_client.generate(
+            text=text,
+            voice="Rachel",
+            model="eleven_monolingual_v2"
+        )
+        
+        # Create a temporary file
         with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as tmp:
-            tts.save(tmp.name)
-            return tmp.name
+            for chunk in audio:
+                if chunk:
+                    tmp.write(chunk)
+            tmp_path = tmp.name
+        
+        return tmp_path
     except Exception as e:
-        gr.Warning(f"gTTS Error: {e}")
+        gr.Warning(f"TTS Error: {str(e)}")
         return None
 
+# Gradio Interface
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("<h1 style='font-size: 3em; text-align: center;'>🚀 Ready Set Hire</h1>")
-
+    gr.Markdown("## Ready Set Hire")
+    gr.Markdown("Upload your resume and practice general interview questions with AI feedback")
+    
     with gr.Tab("📄 Resume Analysis"):
         with gr.Row():
             with gr.Column():
-                resume_upload = gr.File(label="📄 Upload Resume (PDF)", file_types=[".pdf"])
-                process_btn = gr.Button("🔍 Analyze Resume", variant="primary")
+                resume_upload = gr.File(
+                    label="Upload Resume (PDF)",
+                    file_types=[".pdf"],
+                    elem_id="resume-upload"
+                )
+                process_btn = gr.Button("Analyze Resume", variant="primary")
             with gr.Column():
-                resume_summary = gr.Textbox(label="📝 Resume Summary", lines=10)
-                hear_summary_btn = gr.Button("🔊 Hear Summary")
-                summary_audio = gr.Audio(visible=True)
-                ats_score = gr.Textbox(label="📊 ATS Compatibility Score", interactive=False)
-        process_btn.click(fn=process_resume, inputs=resume_upload, outputs=[resume_summary, ats_score])
-        hear_summary_btn.click(fn=gtts_speak, inputs=resume_summary, outputs=summary_audio)
-
+                resume_summary = gr.Textbox(label="Resume Summary", lines=10)
+                ats_score = gr.Textbox(
+                    label="ATS Compatibility Score",
+                    interactive=False,
+                    elem_classes=["ats-score"]
+                )
+        process_btn.click(
+            fn=process_resume,
+            inputs=resume_upload,
+            outputs=[resume_summary, ats_score]
+        )
+    
     with gr.Tab("🎤 Mock Interview"):
         with gr.Row():
             with gr.Column():
-                audio_input = gr.Audio(
-                    label="🎤 Record Your Response",
-                    sources=["microphone"],
-                    type="filepath",
-                    interactive=True
-                )
-                transcribe_btn = gr.Button("📝 Transcribe Response")
-                question_box = gr.Textbox(label="❓ Current Question")
-                generate_btn = gr.Button("🤖 Generate New Question")
-                gtts_question_btn = gr.Button("🔊 Hear Question")
-                question_audio = gr.Audio(visible=True)
+                audio_input = gr.Audio(sources=["microphone"], type="filepath")
+                transcribe_btn = gr.Button("Transcribe Response", variant="primary")
+                question_box = gr.Textbox(label="Current Question")
+                generate_btn = gr.Button("Generate New Question")
             with gr.Column():
-                transcription = gr.Textbox(label="💬 Your Response")
-                evaluation = gr.Textbox(label="📝 Feedback", lines=8)
-                gtts_feedback_btn = gr.Button("🔊 Hear Feedback")
-                feedback_audio = gr.Audio(visible=True)
-
-        transcribe_btn.click(fn=transcribe_audio, inputs=audio_input, outputs=transcription)
-        generate_btn.click(fn=generate_question, inputs=resume_summary, outputs=question_box)
-        transcription.change(fn=evaluate_response, inputs=[question_box, transcription], outputs=evaluation)
-        gtts_question_btn.click(fn=gtts_speak, inputs=question_box, outputs=question_audio)
-        gtts_feedback_btn.click(fn=gtts_speak, inputs=evaluation, outputs=feedback_audio)
+                transcription = gr.Textbox(label="Your Response")
+                evaluation = gr.Textbox(label="Feedback", lines=8)
+                feedback_audio = gr.Audio(label="Feedback Audio", visible=False)
+        
+        # Event handlers
+        transcribe_btn.click(
+            fn=transcribe_audio,
+            inputs=audio_input,
+            outputs=transcription
+        )
+        
+        generate_btn.click(
+            fn=generate_question,
+            inputs=resume_summary,
+            outputs=question_box
+        )
+        
+        gr.on(
+            triggers=[transcription.change],
+            fn=evaluate_response,
+            inputs=[question_box, transcription],
+            outputs=evaluation
+        )
 
-    gr.Markdown("""
-    <div style='text-align:center; margin-top:2em; color:gray'>
-      🚀 Built by Cognify.AI
-    </div>
-    """)
 
 if __name__ == "__main__":
     demo.launch()