Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import requests
 import os
 import random
+import re
 
 HF_TOKEN = os.getenv("HF_TOKEN")
 
@@ -11,58 +12,119 @@ headers = {
     "Authorization": f"Bearer {HF_TOKEN}"
 }
 
-def generate_question(role):
-    prompt = f"Generate one technical interview question for a {role} role."
-    
+# ----------------------------
+# AI Question Generator
+# ----------------------------
+def generate_question(role, difficulty, resume_text):
+    base_prompt = f"Generate one {difficulty} level technical interview question for a {role} role."
+
+    if resume_text.strip() != "":
+        base_prompt += f" The candidate resume mentions: {resume_text}. Ask a question based on that."
+
     payload = {
-        "inputs": prompt,
-        "parameters": {"max_new_tokens": 80}
+        "inputs": base_prompt,
+        "parameters": {"max_new_tokens": 100}
     }
 
     response = requests.post(API_URL, headers=headers, json=payload)
-    
+
     if response.status_code != 200:
-        return "Error generating question. Check your HF_TOKEN."
+        return "Error generating question."
 
     result = response.json()
-    
+
     if isinstance(result, list):
         return result[0]["generated_text"]
-    
+
     return "Could not generate question."
 
+
+# ----------------------------
+# AI Answer Evaluation + Score
+# ----------------------------
 def evaluate_answer(answer):
-    feedback_prompt = f"Evaluate this interview answer and give short feedback:\nAnswer: {answer}"
-    
+    feedback_prompt = f"""
+    Evaluate this interview answer.
+    Give:
+    1. Short feedback
+    2. Score out of 10
+
+    Answer: {answer}
+    """
+
     payload = {
         "inputs": feedback_prompt,
-        "parameters": {"max_new_tokens": 120}
+        "parameters": {"max_new_tokens": 150}
     }
 
     response = requests.post(API_URL, headers=headers, json=payload)
 
     if response.status_code != 200:
-        return "Error generating feedback."
+        return "Error generating feedback.", "0/10"
 
     result = response.json()
 
     if isinstance(result, list):
-        return result[0]["generated_text"]
+        text = result[0]["generated_text"]
+
+        # Try extracting score
+        score_match = re.search(r"\b([0-9]|10)/10\b", text)
+        score = score_match.group(0) if score_match else "N/A"
+
+        return text, score
 
-    return "Could not evaluate answer."
+    return "Could not evaluate.", "0/10"
 
+
+# ----------------------------
+# Gradio UI
+# ----------------------------
 with gr.Blocks() as demo:
     gr.Markdown("# 🤖 Smart Interview Simulator (AI Voice Bot)")
-    
-    role = gr.Textbox(label="Enter Job Role (e.g., Data Scientist)")
-    question_output = gr.Textbox(label="Interview Question")
-    answer_input = gr.Textbox(label="Your Answer")
-    feedback_output = gr.Textbox(label="AI Feedback")
+    gr.Markdown("AI-powered role-based interview practice with scoring and resume-based questions.")
+
+    with gr.Row():
+        role = gr.Textbox(label="Job Role (e.g., Data Scientist)")
+        difficulty = gr.Dropdown(
+            ["Easy", "Medium", "Hard"],
+            label="Select Difficulty",
+            value="Medium"
+        )
+
+    resume_input = gr.Textbox(
+        label="Paste Resume Skills / Summary (Optional)",
+        lines=4
+    )
+
+    question_output = gr.Textbox(label="Interview Question", lines=3)
 
     generate_btn = gr.Button("Generate Question")
+
+    gr.Markdown("## 🎤 Answer Section")
+
+    audio_input = gr.Audio(
+        sources=["microphone"],
+        type="filepath",
+        label="Speak Your Answer (Voice Input)"
+    )
+
+    answer_input = gr.Textbox(label="OR Type Your Answer", lines=4)
+
     evaluate_btn = gr.Button("Evaluate Answer")
 
-    generate_btn.click(generate_question, inputs=role, outputs=question_output)
-    evaluate_btn.click(evaluate_answer, inputs=answer_input, outputs=feedback_output)
+    feedback_output = gr.Textbox(label="AI Feedback", lines=5)
+    score_output = gr.Textbox(label="Score")
+
+    generate_btn.click(
+        generate_question,
+        inputs=[role, difficulty, resume_input],
+        outputs=question_output
+    )
+
+    evaluate_btn.click(
+        evaluate_answer,
+        inputs=answer_input,
+        outputs=[feedback_output, score_output]
+    )
 
 demo.launch()