initial

modules/llm_handler.py

@@ -1,36 +1,63 @@
 # modules/llm_handler.py
-import os
-from groq import Groq
+import ollama
+import config
+import regex as re
 from modules.web_search import search_for_example_answers
 
-# Initialize the Groq client
-# The API key will be automatically read from the HF Space's secrets
-client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
-
 def generate_question(interview_type, document_text):
-    prompt = f"As an expert {interview_type} interviewer, ask one relevant question based on this document: --- {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:
-        chat_completion = client.chat.completions.create(
-            messages=[{"role": "user", "content": prompt}],
-            model="llama3-8b-8192",
-        )
-        return chat_completion.choices[0].message.content
+        response = ollama.chat(model=config.OLLAMA_MODEL, messages=[{'role': 'user', 'content': prompt}])
+        return response['message']['content'].strip()
     except Exception as e:
         return f"Error generating question: {e}"
 
 def evaluate_answer(question, answer):
-    example_answers = search_for_example_answers(question)
     prompt = f"""
-    You are an interview coach. Compare the candidate's answer to the expert examples and provide a concise evaluation.
-    Question: "{question}"
-    Candidate's Answer: "{answer}"
-    Expert Examples: --- {example_answers} ---
+    You are an interview coach. Your task is to evaluate a candidate's answer.
+    You MUST provide a score from 1-10 for each of the following three categories.
+    The format MUST be exactly `Category: [SCORE]/10`.
+
+    Factual Accuracy: [SCORE]/10
+    Relevance & Directness: [SCORE]/10
+    Structure & Clarity: [SCORE]/10
+    
+    After the scores, you MUST provide a brief written evaluation and suggest improvements.
+    """
+    try:
+        response = ollama.chat(model=config.OLLAMA_MODEL, messages=[{'role': 'user', 'content': prompt}], stream=False)
+        return response['message']['content']
+    except Exception as e:
+        return f"An error occurred during evaluation: {e}"
+
+def parse_scores_from_evaluation(evaluation_text: str) -> dict:
+    scores = {
+        'Factual Accuracy': 0,
+        'Relevance & Directness': 0,
+        'Structure & Clarity': 0
+    }
+    pattern = r"(Factual Accuracy|Relevance & Directness|Structure & Clarity):\s*\[?(\d{1,2})\]?\/10"
+    matches = re.findall(pattern, evaluation_text, re.IGNORECASE)
+    
+    for match in matches:
+        category_name, score_value = match[0].strip(), int(match[1])
+        if category_name in scores:
+            scores[category_name] = score_value
+            
+    print(f"📊 Parsed scores: {scores}")
+    return scores
+
+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".
+    **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:
-        chat_completion = client.chat.completions.create(
-            messages=[{"role": "user", "content": prompt}],
-            model="llama3-8b-8192",
-        )
-        return chat_completion.choices[0].message.content
+        response = ollama.chat(model=config.OLLAMA_MODEL, messages=[{'role': 'user', 'content': prompt}], stream=False)
+        return response['message']['content']
     except Exception as e:
-        return f"An error occurred during evaluation: {e}"
+        return "Could not generate holistic feedback due to an error."

modules/report_generator.py

@@ -1,91 +1,54 @@
 # modules/report_generator.py
-
 import datetime
 import os
 import numpy as np
 import matplotlib.pyplot as plt
 from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak, Image, Frame, PageTemplate
 from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
-from reportlab.lib.enums import TA_JUSTIFY, TA_CENTER, TA_LEFT
+from reportlab.lib.enums import TA_JUSTIFY, TA_CENTER
 from reportlab.lib.units import inch
 from reportlab.lib import colors
 from modules.llm_handler import generate_holistic_feedback, parse_scores_from_evaluation
 import config
 
-# --- Page Template with Header and Footer (Unchanged) ---
-class ReportPageTemplate(PageTemplate):
-    def __init__(self, id, pagesize):
-        frame = Frame(inch, inch, pagesize[0] - 2 * inch, pagesize[1] - 2 * inch, id='normal')
-        PageTemplate.__init__(self, id, [frame])
-
-    def beforeDrawPage(self, canvas, doc):
-        canvas.saveState()
-        canvas.setFont('Helvetica', 9)
-        canvas.setFillColor(colors.grey)
-        footer_text = f"Page {doc.page} | AI Interview Coach Report | Generated on {datetime.datetime.now().strftime('%Y-%m-%d')}"
-        canvas.drawCentredString(doc.width / 2 + inch, 0.75 * inch, footer_text)
-        canvas.restoreState()
-
 def create_radar_chart(labels, scores, file_path):
-    # This function is unchanged
+    """Generates and saves a radar chart as a PNG image."""
     num_vars = len(labels)
     angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()
     scores += scores[:1]
     angles += angles[:1]
     fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
-    ax.fill(angles, scores, color='#4A90E2', alpha=0.2)
-    ax.plot(angles, scores, color='#4A90E2', linewidth=2, linestyle='solid')
+    ax.fill(angles, scores, color='#4A90E2', alpha=0.25)
+    ax.plot(angles, scores, color='#4A90E2', linewidth=2)
     ax.set_yticklabels([])
     ax.set_xticks(angles[:-1])
-    ax.set_xticklabels(labels, size=12, color='grey')
-    ax.set_rlabel_position(30)
-    ax.set_ylim(0, 10)
-    for angle, score in zip(angles[:-1], scores[:-1]):
-        ax.text(angle, score + 1.5, str(score), ha='center', va='center', size=14, color="#000000", weight='bold')
-    plt.title('Performance Snapshot', size=20, color='#333333', y=1.1)
-    os.makedirs(os.path.dirname(file_path), exist_ok=True)
-    plt.savefig(file_path, transparent=True, dpi=150)
+    ax.set_xticklabels(labels)
+    plt.savefig(file_path, transparent=True)
     plt.close(fig)
     print(f"📈 Radar chart saved to {file_path}")
 
 def generate_pdf_report(interview_data, file_path):
-    doc = SimpleDocTemplate(file_path, pagesize=(8.5 * inch, 11 * inch),
-                            leftMargin=inch, rightMargin=inch, topMargin=inch, bottomMargin=inch)
-    doc.addPageTemplates([ReportPageTemplate('main_template', (8.5 * inch, 11 * inch))])
-    
+    doc = SimpleDocTemplate(file_path, pagesize=(8.5 * inch, 11 * inch))
     styles = getSampleStyleSheet()
-    
-    # --- THIS IS THE CORRECTED SECTION ---
-    # Instead of adding styles with existing names, we create new ones with unique names.
     styles.add(ParagraphStyle(name='ReportTitle', parent=styles['h1'], fontSize=28, alignment=TA_CENTER, spaceAfter=24))
-    styles.add(ParagraphStyle(name='ReportSubTitle', parent=styles['h2'], fontSize=16, alignment=TA_CENTER, spaceAfter=12, textColor=colors.HexColor('#555555')))
-    styles.add(ParagraphStyle(name='Justify', alignment=TA_JUSTIFY, spaceAfter=12, leading=14))
-    styles.add(ParagraphStyle(name='MainHeader', parent=styles['h1'], fontSize=22, spaceBefore=12, spaceAfter=20, alignment=TA_LEFT, textColor=colors.HexColor('#2c3e50')))
-    styles.add(ParagraphStyle(name='QuestionTitle', parent=styles['h2'], spaceBefore=20, spaceAfter=10, textColor=colors.HexColor('#2980b9')))
-    styles.add(ParagraphStyle(name='SectionTitle', parent=styles['h3'], spaceBefore=12, spaceAfter=6, textColor=colors.HexColor('#34495e')))
-    # --- END OF CORRECTION ---
-
+    styles.add(ParagraphStyle(name='Justify', alignment=TA_JUSTIFY, spaceAfter=12))
+    styles.add(ParagraphStyle(name='QuestionTitle', parent=styles['h2'], spaceBefore=20, spaceAfter=10))
+    styles.add(ParagraphStyle(name='SectionTitle', parent=styles['h3'], spaceBefore=12, spaceAfter=6, textColor=colors.darkblue))
+    
     story = []
-
-    # --- 1. The Title Page ---
     story.append(Paragraph("Interview Performance Report", styles['ReportTitle']))
-    story.append(Spacer(1, 0.5 * inch))
-    story.append(Paragraph(f"Prepared for: <b>{interview_data.get('name', 'N/A')}</b>", styles['ReportSubTitle']))
-    story.append(Spacer(1, 0.2 * inch))
-    story.append(Paragraph(f"Interview Type: <b>{interview_data['type']}</b>", styles['ReportSubTitle']))
-    story.append(Spacer(1, 0.2 * inch))
-    story.append(Paragraph(f"Date of Report: <b>{datetime.datetime.now().strftime('%B %d, %Y')}</b>", styles['ReportSubTitle']))
     story.append(PageBreak())
 
-    # --- 2. Overall Performance & Graph Section ---
-    story.append(Paragraph("Overall Performance Analysis", styles['MainHeader']))
+    story.append(Paragraph("Overall Performance Analysis", styles['h1']))
     full_log_text = ""
     all_scores = []
     skill_labels = ['Factual Accuracy', 'Relevance & Directness', 'Structure & Clarity']
+    
     for i, qa in enumerate(interview_data['q_and_a']):
         full_log_text += f"Q{i+1}: {qa['question']}\nA: {qa['answer']}\n---\n"
         scores = parse_scores_from_evaluation(qa['evaluation'])
         all_scores.append([scores.get(label, 0) for label in skill_labels])
+
     holistic_feedback = generate_holistic_feedback(full_log_text).replace('\n', '<br/>')
     story.append(Paragraph(holistic_feedback, styles['Justify']))
     story.append(Spacer(1, 0.3 * inch))
@@ -95,20 +58,16 @@ def generate_pdf_report(interview_data, file_path):
         chart_path = os.path.join(config.REPORT_FOLDER, "skill_chart.png")
         if os.path.exists(chart_path): os.remove(chart_path)
         create_radar_chart(skill_labels, avg_scores, chart_path)
-        story.append(Image(chart_path, width=4.5*inch, height=4.5*inch, hAlign='CENTER'))
+        story.append(Image(chart_path, width=4*inch, height=4*inch, hAlign='CENTER'))
     story.append(PageBreak())
 
-    # --- 3. Detailed Question-by-Question Analysis ---
-    story.append(Paragraph("Detailed Question Analysis", styles['MainHeader']))
+    story.append(Paragraph("Detailed Question Analysis", styles['h1']))
     for i, qa in enumerate(interview_data['q_and_a']):
         story.append(Paragraph(f"Question {i+1}: {qa['question']}", styles['QuestionTitle']))
         story.append(Paragraph("Your Answer:", styles['SectionTitle']))
         story.append(Paragraph(qa.get('answer', 'N/A'), styles['Justify']))
         story.append(Paragraph("AI Evaluation:", styles['SectionTitle']))
         story.append(Paragraph(qa.get('evaluation', 'N/A').replace('\n', '<br/>'), styles['Justify']))
-
-    try:
-        doc.build(story)
-        print(f"\n✅ Professional report generated successfully: {file_path}")
-    except Exception as e:
-        print(f"💥 Error generating professional PDF report: {e}")
+        
+    doc.build(story)
+    print(f"\n✅ Data-driven report generated successfully: {file_path}")