Create app.py

app.py

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+import gradio as gr
+import os
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from groq import Groq
+from dotenv import load_dotenv
+from faster_whisper import WhisperModel
+from elevenlabs.client import ElevenLabs
+from elevenlabs import play
+import tempfile
+
+# Load environment variables
+load_dotenv()
+
+# 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)
+
+# 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 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",
+        temperature=0,
+    )
+    try:
+        return int(response.choices[0].message.content.strip())
+    except:
+        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: {str(e)}")
+        return f"Error: {str(e)}", "ATS Score: N/A"
+
+def transcribe_audio(audio_path):
+    """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 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 evaluation.choices[0].message.content
+
+def speak_feedback(text):
+    """Convert text feedback to speech"""
+    try:
+        if not text.strip():
+            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:
+            for chunk in audio:
+                if chunk:
+                    tmp.write(chunk)
+            tmp_path = tmp.name
+        
+        return tmp_path
+    except Exception as e:
+        gr.Warning(f"TTS Error: {str(e)}")
+        return None
+
+# Gradio Interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    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"],
+                    elem_id="resume-upload"
+                )
+                process_btn = gr.Button("Analyze Resume", variant="primary")
+            with gr.Column():
+                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(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)
+                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
+        )
+
+
+if __name__ == "__main__":
+    demo.launch()