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backend/app/services/gemini_service.py

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+import logging
+import json
+from google import genai
+from google.genai import types
+from app.config import get_settings
+from app.models.question import QuestionItem
+from app.models.pronunciation import PronunciationItem
+
+settings = get_settings()
+
+client = None
+if settings.gemini_api_key:
+    client = genai.Client(api_key=settings.gemini_api_key)
+
+async def generate_interview_questions(resume_text: str, role_name: str, skills: list[str], experience: int, projects: list[dict] = None) -> list[dict]:
+    if not client:
+        logging.error("Gemini API key not configured.")
+        raise ValueError("Gemini API key not configured")
+
+    # Format projects for the prompt
+    projects_section = ""
+    if projects:
+        projects_section = "\n    Candidate's Projects (PRIORITIZE these in your questions):\n"
+        for p in projects[:5]:
+            tech_str = ", ".join(p.get("tech", []))
+            projects_section += f"    - {p['name']}: {p.get('description', '')[:300]} (Tech: {tech_str})\n"
+    else:
+        projects_section = "    No projects listed."
+
+    prompt = f"""
+    You are an expert technical interviewer.
+    
+    Job Requirement:
+    - Role: {role_name}
+    - Required Experience: {experience} years
+    - Target Skills: {', '.join(skills)}
+    
+    {projects_section}
+    
+    Full Candidate Resume Text:
+    {resume_text}
+    
+    Generate exactly 5 interview questions following this distribution:
+    - Questions 1, 2, 3: MUST be directly about the candidate's specific PROJECTS listed above.
+      Ask about technical decisions made, challenges faced, architecture, or improvements they would make.
+    - Questions 4, 5: Should cover general skills from the job requirement.
+    
+    For each question, provide:
+    1. question: The question itself.
+    2. expected_answer: Key points an ideal answer should cover.
+    3. hint: A short hint if the candidate gets stuck.
+    4. difficulty: Easy, Medium, or Hard.
+    5. category: Technical, Behavioral, or System Design.
+    """
+
+    try:
+        response = client.models.generate_content(
+            model='gemini-2.5-flash',
+            contents=prompt,
+            config=types.GenerateContentConfig(
+                response_mime_type="application/json",
+                response_schema=list[QuestionItem],
+                temperature=0.7,
+            ),
+        )
+        questions = json.loads(response.text)
+        return questions
+    except Exception as e:
+        logging.error(f"Failed to generate questions: {e}")
+        raise ValueError(f"Failed to generate questions: {e}")
+
+async def get_word_pronunciation(word: str) -> dict:
+    if not client:
+        logging.error("Gemini API key not configured.")
+        raise ValueError("Gemini API key not configured")
+
+    prompt = f"""
+    You are an expert linguist and technical dictionary.
+    Analyze the technical term: "{word}"
+    
+    Provide the following structured information:
+    1. word: The correctly spelled word.
+    2. phonetic: The exact IPA (International Phonetic Alphabet) pronunciation (e.g., /ˌpɒlɪˈmɔːfɪzəm/).
+    3. syllables: An array of strings representing the syllable breakdown (e.g., ["pol", "y", "mor", "phism"]).
+    4. definition: A concise definition of the word, specifically in a computer science or tech context (max 2 sentences).
+    """
+
+    try:
+        response = client.models.generate_content(
+            model='gemini-2.5-flash',
+            contents=prompt,
+            config=types.GenerateContentConfig(
+                response_mime_type="application/json",
+                response_schema=PronunciationItem,
+                temperature=0.1,
+            ),
+        )
+        
+        # Parse the JSON response
+        data = json.loads(response.text)
+        return data
+    except Exception as e:
+        logging.error(f"Failed to get pronunciation: {e}")
+        raise ValueError(f"Failed to get pronunciation: {e}")