Update app.py

app.py

@@ -5,6 +5,7 @@ import os
 import gradio_client.utils as client_utils
 import sys
 
+# ======== Patch for Gradio JSON Schema Bug ========
 def _patched_json_schema_to_python_type(schema, defs=None, depth=0):
     if depth > 100:
         return "Any"
@@ -18,18 +19,19 @@ def _patched_json_schema_to_python_type(schema, defs=None, depth=0):
 client_utils._json_schema_to_python_type = _patched_json_schema_to_python_type
 sys.setrecursionlimit(10000)
 
-# Set up device
+# ======== Setup Device ========
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-#access_token = os.getenv["HF_TOKEN"]
-
-# Load model and tokenizer
+# ======== Load Model and Tokenizer ========
 model_name = "AI-Mock-Interviewer/T5"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = T5ForConditionalGeneration.from_pretrained(model_name)
 
+# Fix for attention mask warning
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
 
-# System prompt to guide the interview generation
+# ======== System Prompt ========
 system_prompt = """
 You are conducting a mock technical interview. Generate questions and follow-up questions based on the domain provided. Consider these aspects:
 1. The question should be relevant to the domain (e.g., software engineering, machine learning).
@@ -41,7 +43,7 @@ You are conducting a mock technical interview. Generate questions and follow-up
 Important: Ensure that each question is clear, concise, and allows the candidate to demonstrate their technical and communicative abilities effectively.
 """
 
-# Define sub-topic categories for different domains
+# ======== Subtopic Keywords ========
 subtopic_keywords = {
     "data analysis": ["data cleaning", "missing data", "outliers", "feature engineering", "EDA", "trend analysis", "data visualization"],
     "machine learning": ["supervised learning", "unsupervised learning", "model evaluation", "bias-variance tradeoff", "overfitting", "hyperparameter tuning"],
@@ -49,7 +51,6 @@ subtopic_keywords = {
 }
 
 def identify_subtopic(question, domain):
-    """Identify the sub-topic of a question using predefined keywords."""
     domain = domain.lower()
     if domain in subtopic_keywords:
         for subtopic in subtopic_keywords[domain]:
@@ -58,7 +59,6 @@ def identify_subtopic(question, domain):
     return None
 
 def generate_question(prompt, domain, state=None, max_attempts=10):
-    """Generate a unique interview question while ensuring no repetition."""
     attempts = 0
     while attempts < max_attempts:
         attempts += 1
@@ -73,15 +73,14 @@ def generate_question(prompt, domain, state=None, max_attempts=10):
             top_p=0.9,
             temperature=0.7,
             do_sample=True,
-            pad_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
         )
         question = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
         if not question.endswith("?"):
             question = question.split("?")[0] + "?"
-        
+
         subtopic = identify_subtopic(question, domain)
 
-        # Ensure uniqueness within the session state
         if state:
             if question not in state["asked_questions"] and (subtopic is None or subtopic not in state["asked_subtopics"]):
                 state["asked_questions"].add(question)
@@ -92,55 +91,49 @@ def generate_question(prompt, domain, state=None, max_attempts=10):
     raise RuntimeError("Failed to generate a unique question after multiple attempts.")
 
 def reset_state(domain, company):
-    """Reset session state for a new interview."""
     return {
         "domain": domain,
         "company": company,
         "asked_questions": set(),
         "asked_subtopics": set(),
-        "conversation": []  # List of tuples: (speaker, message)
+        "conversation": []  # List of dicts: {"role": ..., "content": ...}
     }
 
 def start_interview(domain, company):
-    """Start a new interview session."""
     state = reset_state(domain, company)
     prompt = f"Domain: {domain}. " + (f"Company: {company}. " if company else "") + "Generate the first question:"
     question = generate_question(prompt, domain, state)
-    state["conversation"].append(("Interviewer", question))
+    state["conversation"].append({"role": "assistant", "content": question})
     return state["conversation"], state
 
 def submit_response(candidate_response, state):
-    """Accept the candidate's response, update the conversation, and generate a follow-up question."""
-    state["conversation"].append(("Candidate", candidate_response))
+    state["conversation"].append({"role": "user", "content": candidate_response})
     prompt = f"Domain: {state['domain']}. Candidate's last response: {candidate_response}. Generate a follow-up question with a new perspective:"
     question = generate_question(prompt, state["domain"], state)
-    state["conversation"].append(("Interviewer", question))
+    state["conversation"].append({"role": "assistant", "content": question})
     return state["conversation"], state
 
-# Build an interactive Gradio interface using Blocks
+# ======== Gradio Interface ========
 with gr.Blocks() as demo:
-    gr.Markdown("# Interactive Mock Interview")
-    
+    gr.Markdown("# 🎙️ Interactive Mock Interview")
+
     with gr.Row():
         domain_input = gr.Textbox(label="Domain")
         company_input = gr.Textbox(label="Company (Optional)")
-    
-    start_button = gr.Button("Start Interview")
-    chatbot = gr.Chatbot(label="Interview Conversation")
-    
+
+    start_button = gr.Button("🚀 Start Interview")
+    chatbot = gr.Chatbot(label="Interview Conversation", type="messages")
+
     with gr.Row():
         response_input = gr.Textbox(label="Your Response")
         submit_button = gr.Button("Submit")
-    
-    # Maintain session state across interactions
-    state = gr.State({})  # Initialize state properly
-    
-    # Clicking start initializes the interview and shows the first question
+
+    state = gr.State({})  # Session state holder
+
+    # Hook buttons to logic
     start_button.click(start_interview, inputs=[domain_input, company_input], outputs=[chatbot, state])
-    
-    # Submitting a response updates the conversation with a follow-up question
     submit_button.click(submit_response, inputs=[response_input, state], outputs=[chatbot, state]).then(
-        lambda _: "", inputs=[response_input], outputs=[response_input]  # Clear response input after submission
+        lambda: "", inputs=[], outputs=[response_input]  # Clear textbox after submit
     )
 
-demo.launch()
+demo.launch()