Update resume_prompt.py

resume_prompt.py

@@ -1,72 +1,77 @@
-def analyze_resume(resume_text,pipe):
-    # resume_text = truncate_text(resume_text)  # Ensure Resume fits within token limit
-    # modularize resume into personal_information, summary,skills, experience, education, certifications, projects, total_experience, primary_job_function and additional information
-         
-    prompt = f"""
-
-    Respond **ONLY** with a valid JSON object. **Do NOT include any extra text, explanations, or notes.** The JSON should follow this exact structure:
-
-    {{
-        "personal_info": {{
-            "name": "",
-            "email": "",
-            "phone": "",
-            "location": "",
-            "state": "",
-            "country": "",
-            "linkedin": ""
-        }},
-        "summary": "",
-        "skills": {{"technical_skills": [], "soft_skills": [], "languages": []}},
-        "experience": [
-            {{
-                "company": "",
-                "job_title": "",
-                "duration": "",
-                "responsibilities": []
-            }}
-        ],
-        "education": [
-            {{
-                "degree": "",
-                "institution": "",
-                "graduation_date": ""
-            }}
-        ],
-        "certifications": [],
-        "projects": [
-            {{
-                "name": "",
-                "description": "",
-                "technologies": []
-            }}
-        ],
-        "keywords": [],
-        "additional_information": "",
-        "total_experience": 0,
-        "primary_job_function": "",
-        "parsing_issues": []
-    }}
-    Resume:
-    {resume_text}
-    
-    Instructions:
-    • Return only JSON. No extra text or explanations.
-    • If data is missing, use "Not provided" instead of guessing.
-    • Ensure correct JSON format.
-    Guidelines:
-    1. Fill in relevant details from the resume. Use "Not provided" for missing fields.
-    2. Extract location, state, and country (infer from city if needed).
-    3. Categorize skills properly (e.g., technical vs. soft skills).
-    4. List experiences in reverse order (most recent first).
-    5. Include all job experiences, even without responsibilities.
-    6. Summarize job responsibilities (4-5 key points per job).
-    7. Extract all relevant keywords (skills, tools, methodologies).
-    8. Calculate total experience (round to the nearest half-year).
-    9. Identify the primary job function based on role and skills.
-    10. Log unclear sections in "parsing_issues".
-    """
-    response = pipe(prompt,max_new_tokens=5000, do_sample=True, temperature=0.7)
-
-    return response, prompt
+def analyze_resume(resume_text,pipe):
+    # resume_text = truncate_text(resume_text)  # Ensure Resume fits within token limit
+    # modularize resume into personal_information, summary,skills, experience, education, certifications, projects, total_experience, primary_job_function and additional information
+         
+    prompt = f"""
+
+    Respond **ONLY** with a valid JSON object. **Do NOT include any extra text, explanations, or notes.** The JSON should follow this exact structure:
+
+    {{
+        "personal_info": {{
+            "name": "",
+            "email": "",
+            "phone": "",
+            "location": "",
+            "state": "",
+            "country": "",
+            "linkedin": ""
+        }},
+        "summary": "",
+        "skills": {{"technical_skills": [], "soft_skills": [], "languages": []}},
+        "experience": [
+            {{
+                "company": "",
+                "job_title": "",
+                "duration": "",
+                "responsibilities": []
+            }}
+        ],
+        "education": [
+            {{
+                "degree": "",
+                "institution": "",
+                "graduation_date": ""
+            }}
+        ],
+        "certifications": [],
+        "projects": [
+            {{
+                "name": "",
+                "description": "",
+                "technologies": []
+            }}
+        ],
+        "keywords": [],
+        "additional_information": "",
+        "total_experience": 0,
+        "primary_job_function": "",
+        "parsing_issues": []
+    }}
+    Resume:
+    {resume_text}
+    
+    Instructions:
+    • Return only JSON. No extra text or explanations.
+    • If data is missing, use "Not provided" instead of guessing.
+    • Ensure correct JSON format.
+    Guidelines:
+    1. Fill in relevant details from the resume. Use "Not provided" for missing fields.
+    2. Extract location, state, and country (infer from city if needed).
+    3. Categorize skills properly (e.g., technical vs. soft skills).
+    4. List experiences in reverse order (most recent first).
+    5. Include all job experiences, even without responsibilities.
+    6. Summarize job responsibilities (4-5 key points per job).
+    7. Extract all relevant keywords (skills, tools, methodologies).
+    8. Calculate total experience (round to the nearest half-year).
+    9. Identify the primary job function based on role and skills.
+    10. Log unclear sections in "parsing_issues".
+    """
+    # response = pipe(prompt,max_new_tokens=5000, do_sample=True, temperature=0.7)
+    
+    messages = [
+        {"role": "system", "content": "You are an expert resume analyzer capable of understanding various resume formats and extracting relevant information accurately."},
+        {"role": "user", "content": prompt},
+    ]
+    response=pipe(messages,max_new_tokens=5000, do_sample=True, temperature=0.7)
+    return response, prompt
     # return prompt