Update app.py

app.py

@@ -160,20 +160,22 @@ def analyze_symptoms(text, features):
     text = text.lower() if text else ""
     
     # Voice-based health assessment
-    if features["jitter"] > 2.0 or features["shimmer"] > 3.0:
-        feedback.append("Your voice shows signs of irregularity (high jitter or shimmer), which may indicate respiratory or vocal strain. Consider a medical evaluation.")
+    if features["jitter"] > 2.0:
+        feedback.append(f"Elevated jitter ({features['jitter']:.2f}%) detected, which may indicate respiratory strain or vocal cord issues. Consult a doctor.")
+    if features["shimmer"] > 3.0:
+        feedback.append(f"High shimmer ({features['shimmer']:.2f}%) suggests possible emotional stress or vocal fatigue. Consider professional evaluation.")
     if features["energy"] < 0.01:
-        feedback.append("Low vocal energy detected, which might suggest fatigue or low mood. Rest and professional consultation are recommended.")
+        feedback.append(f"Low vocal energy ({features['energy']:.4f}) detected, which might indicate fatigue or low mood. Rest and medical advice recommended.")
     if features["pitch"] < 100 or features["pitch"] > 250:
-        feedback.append(f"Unusual pitch range ({features['pitch']:.2f} Hz) detected, which could indicate vocal cord issues or emotional stress.")
+        feedback.append(f"Unusual pitch ({features['pitch']:.2f} Hz) may indicate vocal cord issues or emotional stress.")
     if features["spectral_centroid"] > 2000:
-        feedback.append("High spectral centroid suggests tense or strained speech, possibly linked to stress or anxiety.")
+        feedback.append(f"High spectral centroid ({features['spectral_centroid']:.2f} Hz) suggests tense speech, possibly linked to stress or anxiety.")
     
     # Text-based symptom analysis
     if "cough" in text or "breath" in text:
         feedback.append("Your description suggests respiratory symptoms. Possible conditions include bronchitis or asthma. Please consult a doctor.")
     if "stress" in text or "anxious" in text:
-        feedback.append("You mentioned stress or anxiety. Try deep breathing exercises or mindfulness. Consider speaking with a mental health professional.")
+        feedback.append("You mentioned stress or anxiety. Try deep breathing or mindfulness. Consider speaking with a mental health professional.")
     if "pain" in text:
         feedback.append("Pain reported. For mild pain, consider Paracetamol; for inflammation, Ibuprofen may help. Consult a doctor before taking medication.")
     if not feedback:
@@ -215,56 +217,62 @@ def store_user_consent(language):
 
 def generate_pdf_report(feedback, transcription, features, language):
     try:
+        # Sanitize inputs for LaTeX
         feedback = feedback.replace('&', '\\&').replace('%', '\\%').replace('$', '\\$').replace('#', '\\#')
         transcription = transcription.replace('&', '\\&').replace('%', '\\%').replace('$', '\\$').replace('#', '\\#') if transcription else "None"
         email = DEFAULT_EMAIL.replace('&', '\\&').replace('%', '\\%').replace('$', '\\$').replace('#', '\\#')
-        latex_content = f"""
-\\documentclass[a4paper,12pt]{{article}}
-\\usepackage[utf8]{{inputenc}}
-\\usepackage{{geometry}}
-\\usepackage{{parskip}}
-\\usepackage{{titlesec}}
-\\usepackage{{times}}
-\\geometry{{margin=1in}}
-\\titleformat{{\\section}}{{\\large\\bfseries}}{{\\thesection}}{{1em}}{{}}
-\\titleformat{{\\subsection}}{{\\bfseries}}{{\\thesubsection}}{{1em}}{{}}
-\\usepackage{{datetime}}
-\\newdateformat{{isodate}}{{\\THEDAY{ }\\shortmonthname[\\THEMONTH] \\THEYEAR}}
-\\begin{{document}}
-\\begin{{center}}
-    \\textbf{{\\large MindCare Health Assistant Report}} \\\\
-    \\vspace{{0.5cm}}
-    Generated on \\isodate\\today\\ at \\currenttime
-\\end{{center}}
-\\section*{{User Information}}
-\\begin{{itemize}}
-    \\item \\textbf{{Email}}: {email}
-    \\item \\textbf{{Language}}: {SALESFORCE_LANGUAGE_MAP.get(language, "English")}
-\\end{{itemize}}
-\\section*{{Voice Analysis Results}}
-\\subsection*{{Health Assessment}}
-{feedback}
-\\subsection*{{Transcription}}
-{transcription}
-\\subsection*{{Voice Metrics}}
-\\begin{{itemize}}
-    \\item \\textbf{{Pitch}}: {features['pitch']:.2f} Hz
-    \\item \\textbf{{Jitter}}: {features['jitter']:.2f}\\%
-    \\item \\textbf{{Shimmer}}: {features['shimmer']:.2f}\\%
-    \\item \\textbf{{Energy}}: {features['energy']:.4f}
-    \\item \\textbf{{MFCC Mean}}: {features['mfcc_mean']:.2f}
-    \\item \\textbf{{Spectral Centroid}}: {features['spectral_centroid']:.2f} Hz
-\\end{{itemize}}
-\\section*{{Disclaimer}}
-This report is a preliminary analysis and not a medical diagnosis. Always consult a healthcare provider.
-\\end{{document}}
-"""
+        language_display = SALESFORCE_LANGUAGE_MAP.get(language, "English")
+        
+        latex_content = (
+            "\\documentclass[a4paper,12pt]{article}\n"
+            "\\usepackage[utf8]{inputenc}\n"
+            "\\usepackage{geometry}\n"
+            "\\usepackage{parskip}\n"
+            "\\usepackage{titlesec}\n"
+            "\\usepackage{times}\n"
+            "\\usepackage{datetime}\n"
+            "\\newdateformat{isodate}{\\THEDAY{} \\shortmonthname[\\THEMONTH] \\THEYEAR}\n"
+            "\\geometry{margin=1in}\n"
+            "\\titleformat{\\section}{\\large\\bfseries}{\\thesection}{1em}{}\n"
+            "\\titleformat{\\subsection}{\\bfseries}{\\thesubsection}{1em}{}\n"
+            "\\begin{document}\n"
+            "\\begin{center}\n"
+            "    \\textbf{\\large MindCare Health Assistant Report} \\\\\n"
+            "    \\vspace{0.5cm}\n"
+            "    Generated on \\isodate\\today\\ at \\currenttime\n"
+            "\\end{center}\n"
+            "\\section*{User Information}\n"
+            "\\begin{itemize}\n"
+            f"    \\item \\textbf{{Email}}: {email}\n"
+            f"    \\item \\textbf{{Language}}: {language_display}\n"
+            "\\end{itemize}\n"
+            "\\section*{Voice Analysis Results}\n"
+            "\\subsection*{Health Assessment}\n"
+            f"{feedback}\n"
+            "\\subsection*{Transcription}\n"
+            f"{transcription}\n"
+            "\\subsection*{Voice Metrics}\n"
+            "\\begin{itemize}\n"
+            f"    \\item \\textbf{{Pitch}}: {features['pitch']:.2f} Hz\n"
+            f"    \\item \\textbf{{Jitter}}: {features['jitter']:.2f}\\%\n"
+            f"    \\item \\textbf{{Shimmer}}: {features['shimmer']:.2f}\\%\n"
+            f"    \\item \\textbf{{Energy}}: {features['energy']:.4f}\n"
+            f"    \\item \\textbf{{MFCC Mean}}: {features['mfcc_mean']:.2f}\n"
+            f"    \\item \\textbf{{Spectral Centroid}}: {features['spectral_centroid']:.2f} Hz\n"
+            "\\end{itemize}\n"
+            "\\section*{Disclaimer}\n"
+            "This report is a preliminary analysis and not a medical diagnosis. Always consult a healthcare provider.\n"
+            "\\end{document}\n"
+        )
+        
         with tempfile.NamedTemporaryFile(delete=False, suffix=".tex") as tex_file:
             tex_file.write(latex_content.encode('utf-8'))
             tex_file_path = tex_file.name
         pdf_path = tex_file_path.replace('.tex', '.pdf')
-        result = subprocess.run(['latexmk', '-pdf', '-pdflatex=pdflatex', '-interaction=nonstopmode', tex_file_path], 
-                              capture_output=True, text=True, check=True)
+        result = subprocess.run(
+            ['latexmk', '-pdf', '-pdflatex=pdflatex', '-interaction=nonstopmode', tex_file_path],
+            capture_output=True, text=True, check=True
+        )
         logger.info(f"PDF generation output: {result.stdout}")
         for ext in ['.aux', '.log', '.out', '.fls', '.fdb_latexmk']:
             try:
@@ -281,7 +289,7 @@ This report is a preliminary analysis and not a medical diagnosis. Always consul
         logger.error(f"PDF generation failed: {e.stderr}")
         return None
     except Exception as e:
-        logger.error(f"PDF generation failed: {str(e)}")
+        logger.error(f"PDF generation failed: { five(str(e))}")
         return None
 
 def store_in_salesforce(user_id, audio_file, feedback, respiratory_score, mental_health_score, features, transcription, language):