Update app.py

app.py

@@ -10,17 +10,17 @@ import os
 
 # -------------------- Core Functions --------------------
 def setup_embeddings():
-    return HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+    return HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 
 def setup_chromadb():
     client = chromadb.PersistentClient(path="./chroma_db")
     return client.get_or_create_collection(name="resumes")
 
 def extract_text_from_resume(file):
-    if file.name.endswith('.pdf'):
+    if file.name.endswith(".pdf"):
         doc = fitz.open(stream=file.read(), filetype="pdf")
         return "\n".join([page.get_text("text") for page in doc])
-    elif file.name.endswith('.txt'):
+    elif file.name.endswith(".txt"):
         return file.read().decode("utf-8")
     return ""
 
@@ -99,7 +99,7 @@ def strict_agent_monitor(candidate_response):
 embedding_model = setup_embeddings()
 collection = setup_chromadb()
 
-# -------------------- Gradio Interface --------------------
+# -------------------- Gradio Application --------------------
 class InterviewCoach:
     def __init__(self):
         self.user_id = str(uuid.uuid4())
@@ -113,28 +113,25 @@ class InterviewCoach:
         self.clarification_response = None
         self.uploaded_file = None
 
-    def process_resume(self, file):
-        self.uploaded_file = file
-        with open(file.name, "rb") as f:
-            resume_text = extract_text_from_resume(f)
-            self.candidate_name = store_resume(resume_text, self.user_id)
-        return f"Resume processed for {self.candidate_name}"
-
-    def start_interview(self):
-        if not self.uploaded_file:
-            return "Please upload a resume first", None
+    def start_interview(self, file):
+        if not file:
+            return "Please upload a resume file first", None, None
         
+        self.uploaded_file = file
         self.interview_active = True
         self.current_step = 0
         self.interview_phase = "greeting"
         self.questions = []
         self.responses = []
         
+        resume_text = extract_text_from_resume(file)
+        self.candidate_name = store_resume(resume_text, self.user_id)
+        
         resume_data = retrieve_resume(self.user_id, "background experience")
         greeting = zero_agent_greeting(resume_data, self.candidate_name)
         self.questions.append(greeting)
         
-        return f"Interview started with {self.candidate_name}", greeting
+        return f"Interview started with {self.candidate_name}", greeting, gr.update(visible=True)
 
     def zero_agent_greeting(self, resume_data, candidate_name):
         prompt = f"""
@@ -146,6 +143,8 @@ class InterviewCoach:
         2. Very briefly mention something from their resume (one skill or experience)
         3. Ask ONE simple question about their most recent job or experience
         4. Keep it extremely concise (2-3 short sentences maximum)
+        
+        The greeting must be brief as it will be converted to voice later.
         """
         return generate_groq_response(prompt, "zero_agent", temperature=0.7)
 
@@ -158,14 +157,19 @@ class InterviewCoach:
         Question Number: {question_count + 1}
         Difficulty: {difficulty}
         
-        Generate a relevant technical interview question based on the candidate's resume.
+        Generate a relevant technical interview question based on the candidate's resume. The question should:
+        1. Be specific to skills or experiences mentioned in their resume
+        2. Feel like it's coming from someone who has read their background
+        3. Be appropriately challenging based on their experience level
+        4. Be directly relevant to their field
+        5. Be clearly phrased as a question (no preambles or explanations)
         """
         return generate_groq_response(prompt, "technical_agent", temperature=0.7)
 
     def clarification_agent_response(self, question, candidate_response, resume_data):
         needs_clarification = any(phrase in candidate_response.lower() for phrase in 
                                 ["i don't understand", "can you explain", "not sure", "what do you mean", 
-                                "confused", "unclear", "can you clarify", "don't know what", "?"])
+                                 "confused", "unclear", "can you clarify", "don't know what", "?"])
         
         if needs_clarification:
             prompt = f"""
@@ -241,18 +245,15 @@ class InterviewCoach:
         feedback = generate_groq_response(prompt, "report_agent", temperature=0.7)
         return strip_markdown(feedback)
 
-    def submit_response(self, answer):
-        if not self.interview_active:
-            return "Interview not active. Please start the interview first.", None, None
-        
+    def process_response(self, answer):
         if not answer.strip():
-            return "Please enter a response.", None, None
+            return "Please provide a response", None, None
         
         appropriateness_check = strict_agent_monitor(answer)
         if "INAPPROPRIATE:" in appropriateness_check:
             reason = appropriateness_check.split("INAPPROPRIATE:")[1].strip()
             self.interview_active = False
-            return f"⚠️ Interview Terminated: {reason}", None, None
+            return f"⚠️ Interview Terminated: {reason}", None, gr.update(visible=False)
         
         current_question = self.questions[self.current_step]
         
@@ -271,7 +272,7 @@ class InterviewCoach:
                 return None, new_question, None
             elif len(self.responses) >= 6:
                 self.interview_active = False
-                return self.generate_final_report(), None, None
+                return self.generate_final_report(), None, gr.update(visible=False)
             else:
                 interview_history = "\n".join([
                     f"Q: {item['question']}\nA: {item['answer']}" 
@@ -313,7 +314,7 @@ class InterviewCoach:
                     return None, new_question, None
                 elif len(self.responses) >= 6:
                     self.interview_active = False
-                    return self.generate_final_report(), None, None
+                    return self.generate_final_report(), None, gr.update(visible=False)
                 else:
                     interview_history = "\n".join([
                         f"Q: {item['question']}\nA: {item['answer']}" 
@@ -386,25 +387,24 @@ class InterviewCoach:
         
         for idx, response in enumerate(processed_feedback):
             report_html += f"""
-            <details style='margin-bottom: 1.5rem; background:#2D2D2D; padding:1rem; border-radius:8px;'>
-                <summary style='color: #FFFFFF; cursor: pointer;'>Question {idx+1}</summary>
-                <div style='margin-top: 1rem;'>
-                    <p style='font-weight: 500; color: #FFFFFF; font-size: 1.1rem;'>❝{response['question']}❞</p>
-                    
-                    <div style='background: #333333; padding:1rem; border-radius:8px; margin:1rem 0;'>
-                        <p style='color: #888888; margin:0;'>Your Answer:</p>
-                        <p style='color: #FFFFFF; margin:0.5rem 0;'>{response['answer']}</p>
+            <div style='background:#2D2D2D; border-radius:10px; padding:1.5rem; margin:1rem 0;'>
+                <details>
+                    <summary style='color: #FFFFFF; font-weight:500; cursor:pointer;'>Question {idx+1}</summary>
+                    <div style='margin-top:1rem;'>
+                        <p style='font-weight: 500; color: #FFFFFF; font-size: 1.1rem;'>❝{response['question']}❞</p>
+                        <div style='background: #333333; padding:1rem; border-radius:8px; margin:1rem 0;'>
+                            <p style='color: #888888; margin:0;'>Your Answer:</p>
+                            <p style='color: #FFFFFF; margin:0.5rem 0;'>{response['answer']}</p>
+                        </div>
+                        {response['correct_html']}
+                        {response['improve_html']}
                     </div>
-                    
-                    {response['correct_html']}
-                    {response['improve_html']}
-                </div>
-            </details>
+                </details>
+            </div>
             """
         
         if topics:
             report_html += """
-            <h4 style='color: #FFFFFF; margin:2rem 0 1rem 0;'>📚 Focus Areas for Improvement</h4>
             <div style="background:#2D2D2D; padding:1.5rem; border-radius:10px; margin:1rem 0;">
                 <h4 style="margin:0; color:#FFFFFF;">Recommended Topics to Study</h4>
                 <p style="margin:1rem 0; color:#CCCCCC;">Based on your interview responses, we recommend focusing on these key areas:</p>
@@ -413,7 +413,7 @@ class InterviewCoach:
             
             for topic in topics:
                 report_html += f"""
-                <span style="display: inline-block; background: #333333; padding: 5px 10px; margin: 5px; border-radius: 15px; font-size: 0.8rem;">{topic}</span>
+                <span style="display:inline-block; background:#333333; padding:5px 10px; margin:5px; border-radius:15px; font-size:0.8rem;">{topic}</span>
                 """
                 
             report_html += """
@@ -424,50 +424,48 @@ class InterviewCoach:
         report_html += "</div>"
         return report_html
 
-# Create the interface
+# Create the Gradio interface
 coach = InterviewCoach()
 
-with gr.Blocks(theme=gr.themes.Default(primary_hue="blue")) as demo:
+with gr.Blocks(theme=gr.themes.Default(primary_hue="blue", secondary_hue="gray")) as demo:
     gr.Markdown("# 💼 AI-Powered Interview Coach")
     gr.Markdown("Upload your resume for a personalized mock interview session")
     
     with gr.Row():
-        with gr.Column():
-            file_input = gr.File(label="Upload Resume (PDF or TXT)", file_types=[".pdf", ".txt"])
-            upload_btn = gr.Button("Upload Resume")
-            upload_status = gr.Textbox(label="Upload Status", interactive=False)
-            
+        with gr.Column(scale=1):
+            file_upload = gr.File(label="Upload Resume (PDF or TXT)", file_types=[".pdf", ".txt"])
             start_btn = gr.Button("🚀 Start Interview Session")
-            interview_status = gr.Textbox(label="Interview Status", interactive=False)
-            
-            question_display = gr.Textbox(label="Current Question", interactive=False)
-            answer_input = gr.Textbox(label="Your Response", lines=5)
-            submit_btn = gr.Button("Submit Response")
-            
-            clarification_display = gr.Textbox(label="Clarification", visible=False, interactive=False)
-            
-            report_display = gr.HTML(label="Interview Report")
-            
-            def toggle_clarification(needs_clarification):
-                return gr.Textbox(visible=needs_clarification)
-            
-        # Event handlers
-        upload_btn.click(
-            fn=coach.process_resume,
-            inputs=file_input,
-            outputs=upload_status
-        )
+            status = gr.Textbox(label="Status", interactive=False)
         
-        start_btn.click(
-            fn=coach.start_interview,
-            outputs=[interview_status, question_display]
-        )
-        
-        submit_btn.click(
-            fn=coach.submit_response,
-            inputs=answer_input,
-            outputs=[interview_status, question_display, report_display]
-        )
+        with gr.Column(scale=2):
+            question_display = gr.Textbox(label="Current Question", interactive=False, lines=3)
+            answer_input = gr.Textbox(label="Your Response", lines=5, visible=False)
+            submit_btn = gr.Button("Submit Response", visible=False)
+            report_display = gr.HTML(label="Interview Report", visible=False)
+    
+    def start_interview(file):
+        return coach.start_interview(file)
+    
+    def process_response(answer):
+        return coach.process_response(answer)
+    
+    start_btn.click(
+        start_interview,
+        inputs=[file_upload],
+        outputs=[status, question_display, answer_input]
+    ).then(
+        lambda: gr.update(visible=True),
+        outputs=[submit_btn]
+    )
+    
+    submit_btn.click(
+        process_response,
+        inputs=[answer_input],
+        outputs=[status, question_display, submit_btn]
+    ).then(
+        lambda report: gr.update(value=report, visible=True) if report else None,
+        inputs=[report_display],
+        outputs=[report_display]
+    )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()