initial

modules/llm_handler.py

@@ -1,16 +1,25 @@
 # modules/llm_handler.py
-import ollama
+import os
 import config
 import regex as re
+from groq import Groq
 from modules.web_search import search_for_example_answers
 
+# Initialize the Groq client
+# It will automatically use the API key from your Hugging Face secrets
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+MODEL = "llama3-8b-8192" # Use Groq's Llama 3 8B model
+
 def generate_question(interview_type, document_text):
     prompt = f"As an expert {interview_type} interviewer, ask one relevant, open-ended question based on this document:\n\n---\n{document_text}\n---"
     try:
-        response = ollama.chat(model=config.OLLAMA_MODEL, messages=[{'role': 'user', 'content': prompt}])
-        return response['message']['content'].strip()
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model=MODEL,
+        )
+        return chat_completion.choices[0].message.content
     except Exception as e:
-        return f"Error generating question: {e}"
+        return f"Error generating question from API: {e}"
 
 def evaluate_answer(question, answer):
     prompt = f"""
@@ -22,11 +31,14 @@ def evaluate_answer(question, answer):
     Relevance & Directness: [SCORE]/10
     Structure & Clarity: [SCORE]/10
     
-    After the scores, you MUST provide a brief written evaluation and suggest improvements.
+    After the scores, you MUST provide a brief written evaluation.
     """
     try:
-        response = ollama.chat(model=config.OLLAMA_MODEL, messages=[{'role': 'user', 'content': prompt}], stream=False)
-        return response['message']['content']
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model=MODEL,
+        )
+        return chat_completion.choices[0].message.content
     except Exception as e:
         return f"An error occurred during evaluation: {e}"
 
@@ -49,15 +61,14 @@ def parse_scores_from_evaluation(evaluation_text: str) -> dict:
 
 def generate_holistic_feedback(full_interview_log):
     prompt = f"""
-    You are a senior interview coach reviewing a candidate's full interview performance.
-    Based on the entire Q&A log, provide a high-level "Overall Performance Summary" and an "Actionable Improvement Plan".
+    You are a senior interview coach. Based on the entire Q&A log, provide a high-level "Overall Performance Summary" and an "Actionable Improvement Plan".
     **FULL INTERVIEW LOG:** --- {full_interview_log} ---
-    **INSTRUCTIONS:**
-    1.  **Overall Performance Summary:** Summarize performance, identifying patterns of strengths and weaknesses.
-    2.  **Actionable Improvement Plan:** Provide a bulleted list of the top 3 most critical actions to take.
     """
     try:
-        response = ollama.chat(model=config.OLLAMA_MODEL, messages=[{'role': 'user', 'content': prompt}], stream=False)
-        return response['message']['content']
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model=MODEL,
+        )
+        return chat_completion.choices[0].message.content
     except Exception as e:
         return "Could not generate holistic feedback due to an error."