Upload app.py

app.py

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+import os
+import re
+import time
+import asyncio
+import tempfile
+import streamlit as st
+import google.generativeai as genai
+import edge_tts
+import speech_recognition as sr
+from dotenv import load_dotenv
+import pandas as pd
+
+# ✅ Streamlit page config
+st.set_page_config(page_title="GrillMaster", layout="wide")
+
+# Load API key
+load_dotenv()
+genai.configure(api_key=os.getenv("GOOGLE_api"))
+
+# -----------------------------
+# SESSION STATE DEFAULTS
+# -----------------------------
+defaults = {
+    "generated_questions": [],
+    "current_question_index": 0,
+    "answers": [],
+    "evaluations": [],
+    "evaluation_feedback": "",
+    "overall_score": 0,
+    "percentage_score": 0,
+    "is_recording": False,
+    "question_played": False,
+    "selected_domain": "",
+    "response_captured": False,
+    "timer_start": None,
+    "show_intro": False,
+    "recorded_text": "",
+    "recording_complete": False,
+    "recording_started": False,
+    "audio_played": False,
+    "question_start_time": 0.0,
+    "record_phase": "",
+    "improvement_suggestions_generated": False,
+    "improvement_suggestions": ""
+}
+for key, value in defaults.items():
+    if key not in st.session_state:
+        st.session_state[key] = value
+
+# -----------------------------
+# QUESTIONS
+# -----------------------------
+CANDIDATE_QUESTIONS = [
+    {"text": "Can you introduce yourself?", "type": "introduction"},
+    {"text": "Why do you want to be a part of Analytics domain?", "type": "introduction"},
+    {"text": "Can you try to explain any project of yours in detail?", "type": "project"},
+    {"text": "Any challenges faced while working on the project?", "type": "project"},
+    {"text": "What could be the business impact of the project?", "type": "project"}
+]
+
+# -----------------------------
+# AUDIO GENERATION
+# -----------------------------
+async def generate_question_audio(question, voice="en-IE-EmilyNeural"):
+    clean_question = re.sub(r'[^A-Za-z0-9.,?! ]+', '', question)
+    tts = edge_tts.Communicate(text=clean_question, voice=voice)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
+        await tts.save(tmp_file.name)
+        return tmp_file.name
+
+# -----------------------------
+# EVALUATION FUNCTION
+# -----------------------------
+def evaluate_answer(question_text, answer_text, q_type):
+    model = genai.GenerativeModel("gemini-pro")
+
+    if q_type == "introduction":
+        prompt = f"""
+You are an expert interviewer evaluating a candidate's introduction. Assess the response based on:
+- Clarity & Fluency
+- Confidence & Professionalism
+- Relevance & Structure
+- Conciseness
+
+Provide an evaluation summary with a score out of 10.
+
+Candidate Introduction:
+{answer_text}
+"""
+    else:  # project explanation
+        prompt = f"""
+You are an expert interviewer evaluating a candidate's project explanation. Assess the response based on:
+- Technical Understanding
+- Communication Clarity
+- Problem-Solving & Impact
+- Use of Examples
+- Logical Flow & Structure
+
+Provide an evaluation summary with a score out of 10.
+
+Candidate Project Explanation:
+{answer_text}
+"""
+    response = model.generate_content(prompt)
+    text = response.text.strip()
+    score_match = re.search(r"\*\*Overall Score:\*\* (\d+)/10", text)
+    score = int(score_match.group(1)) if score_match else 0
+    return {"score": score, "feedback": text}
+
+# -----------------------------
+# IMPROVEMENT SUGGESTIONS
+# -----------------------------
+def generate_improvement_suggestions():
+    model = genai.GenerativeModel('gemini-pro')
+    if not st.session_state.get("answers"):
+        st.session_state.improvement_suggestions = "No answers were recorded to generate improvement suggestions."
+        return
+
+    qa_context = []
+    for i, entry in enumerate(st.session_state["answers"]):
+        qa_context.append(
+            f"Question {i+1}: {entry['question']}\nCandidate's Answer {i+1}: {entry.get('response','[No response]')}"
+        )
+    full_qa_context = "\n\n".join(qa_context)
+
+    prompt = f"""
+You are an interview coach. Based on these Q&A:
+{full_qa_context}
+
+Provide detailed improvement suggestions for each answer. Be constructive and supportive.
+"""
+    try:
+        st.info("🤖 Generating detailed improvement suggestions...")
+        response = model.generate_content(prompt)
+        st.session_state.improvement_suggestions = response.text.strip()
+        st.session_state.improvement_suggestions_generated = True
+        st.success("Detailed suggestions generated!")
+    except Exception as e:
+        st.error(f"Error generating suggestions: {e}")
+        st.session_state.improvement_suggestions_generated = False
+
+# -----------------------------
+# START PAGE: Candidate Intro Button
+# -----------------------------
+if not st.session_state["show_intro"]:
+    st.title("🔥🎯 Welcome to GrillMaster Mock Interview")
+    st.markdown("Click the button below to start the Candidate Introduction + Project mock interview:")
+    if st.button("🎤 Candidate Intro + Project"):
+        st.session_state.update({
+            "show_intro": True,
+            "selected_domain": "Candidate Intro + Project",
+            "current_question_index": 0,
+            "answers": [],
+            "evaluations": [],
+            "question_played": False
+        })
+        st.rerun()
+
+# -----------------------------
+# CANDIDATE INTRO WORKFLOW
+# -----------------------------
+if st.session_state.get("show_intro"):
+    st.header("🎯 Candidate Introduction + Project")
+
+    q_index = st.session_state["current_question_index"]
+    if q_index < len(CANDIDATE_QUESTIONS):
+        question = CANDIDATE_QUESTIONS[q_index]
+        st.subheader(f"Q{q_index+1}: {question['text']}")
+
+        # Generate audio
+        if not st.session_state["question_played"]:
+            audio_file = asyncio.run(generate_question_audio(question["text"]))
+            st.audio(audio_file, format="audio/mp3")
+            st.session_state["question_played"] = True
+
+        # Record answer
+        audio_data = st.audio_input("🎤 Record your answer here")
+        if audio_data:
+            audio_bytes = audio_data.read()
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+                f.write(audio_bytes)
+                wav_path = f.name
+            recognizer = sr.Recognizer()
+            with sr.AudioFile(wav_path) as source:
+                recorded_audio = recognizer.record(source)
+                try:
+                    response_text = recognizer.recognize_google(recorded_audio)
+                    st.session_state["current_response"] = response_text
+                    st.success("✅ Answer recorded. You can re-record or move to next question.")
+                except sr.UnknownValueError:
+                    st.error("⚠️ Could not understand audio.")
+
+        # Buttons
+        col1, col2 = st.columns(2)
+        with col1:
+            if st.button("🔄 Re-record Answer"):
+                st.session_state.pop("current_response", None)
+                st.session_state["question_played"] = False
+                st.experimental_rerun()
+        with col2:
+            if "current_response" in st.session_state and st.button("➡️ Next Question"):
+                st.session_state["answers"].append({
+                    "question": question["text"],
+                    "response": st.session_state.pop("current_response"),
+                    "type": question["type"]
+                })
+                st.session_state["question_played"] = False
+                st.session_state["current_question_index"] += 1
+                st.rerun()
+
+    else:
+        # Evaluate all answers
+        if not st.session_state["evaluations"]:
+            for ans in st.session_state["answers"]:
+                st.session_state["evaluations"].append(evaluate_answer(ans["question"], ans["response"], ans["type"]))
+
+        st.subheader("📊 Mock Interview Completed")
+        total_score = sum([ev["score"] for ev in st.session_state["evaluations"]])
+        overall_score = round(total_score / len(st.session_state["evaluations"]), 2)
+        st.write(f"**Overall Average Score:** {overall_score}/10")
+        st.progress(overall_score / 10)
+
+        # Show answers & feedback
+        for i, ev in enumerate(st.session_state["evaluations"]):
+            st.write(f"**Q{i+1}: {ev['question']}**")
+            st.write(f"**A:** {ev['response']}")
+            st.write(f"**Score:** {ev['score']}/10")
+            st.write(ev["feedback"])
+            st.write("---")
+
+        # Improvement suggestions
+        if st.button("💡 Generate Improvement Suggestions"):
+            generate_improvement_suggestions()
+            st.rerun()
+        if st.session_state.get("improvement_suggestions_generated"):
+            with st.expander("🔍 Improvement Suggestions", expanded=True):
+                st.markdown(st.session_state["improvement_suggestions"])
+
+        # Download summary
+        def prepare_summary():
+            text = "# GrillMaster Candidate Intro Summary\n\n"
+            for i, ans in enumerate(st.session_state["answers"]):
+                text += f"**Q{i+1}: {ans['question']}**\n**A:** {ans['response']}\n**Score:** {st.session_state['evaluations'][i]['score']}/10\n\n"
+            if st.session_state.get("improvement_suggestions_generated"):
+                text += "## Improvement Suggestions:\n" + st.session_state["improvement_suggestions"]
+            return text.encode("utf-8")
+
+        st.download_button("💾 Download Summary", data=prepare_summary(),
+                           file_name=f"GrillMaster_Summary_{time.strftime('%Y%m%d_%H%M')}.md",
+                           mime="text/markdown")