Update app.py

app.py

@@ -90,62 +90,63 @@ def craft_cv(llm, prompt, maxtokens, temperature, top_probability):
     return cv_text, output
 
 def convert_to_json(llm, cv_text, maxtokens, temperature, top_probability):
-    prompt = """
-            You are an expert at structuring resumes in JSON format. Given a modified resume text, extract the relevant details and convert them into the following structured JSON format:
+    # prompt = """
+    #         You are an expert at structuring resumes in JSON format. Given a modified resume text, extract the relevant details and convert them into the following structured JSON format:
             
-            {
-              "profileDetail": {
-                "name": "[Candidate's Name]",
-                "email": "[Candidate's Email]",
-                "phone": "[Candidate's Phone]",
-                "linkedin": "[Candidate's LinkedIn]",
-                "languages": "Hindi,English",
-                "interests": "Cricket",
-                "location": "[Candidate's Location]",
-                "role": "[Candidate's Role]"
-              },
-              "professionalSummary": "[Candidate's Professional Summary]",
-              "skills": ["skill1", "skill2"],
-              "workExperience": [
-                {
-                  "title": "[Job Title]",
-                  "company": "[Company Name]",
-                  "location": "[Location]",
-                  "startDate": "[Start Date]",
-                  "endDate": "[End Date]",
-                  "responsibilities": ["[Responsibility 1]", "[Responsibility 2]"],
-                  "projects": [
-                    {
-                      "title": "[Project Title]",
-                      "description": "[Project Description]"
-                    }
-                  ]
-                }
-              ],
-              "education": [
-                {
-                  "degree": "[Degree]",
-                  "institution": "[Institution]",
-                  "location": "[Location]",
-                  "graduationDate": "[Graduation Date]"
-                }
-              ],
-              "certifications": ["[Certification 1]", "[Certification 2]"] ,
-              "extraCurricular": "[Extra Curricular Activities]",
-              "achievment": "[Achievements]"
-            }
+    #         {
+    #           "profileDetail": {
+    #             "name": "[Candidate's Name]",
+    #             "email": "[Candidate's Email]",
+    #             "phone": "[Candidate's Phone]",
+    #             "linkedin": "[Candidate's LinkedIn]",
+    #             "languages": "Hindi,English",
+    #             "interests": "Cricket",
+    #             "location": "[Candidate's Location]",
+    #             "role": "[Candidate's Role]"
+    #           },
+    #           "professionalSummary": "[Candidate's Professional Summary]",
+    #           "skills": ["skill1", "skill2"],
+    #           "workExperience": [
+    #             {
+    #               "title": "[Job Title]",
+    #               "company": "[Company Name]",
+    #               "location": "[Location]",
+    #               "startDate": "[Start Date]",
+    #               "endDate": "[End Date]",
+    #               "responsibilities": ["[Responsibility 1]", "[Responsibility 2]"],
+    #               "projects": [
+    #                 {
+    #                   "title": "[Project Title]",
+    #                   "description": "[Project Description]"
+    #                 }
+    #               ]
+    #             }
+    #           ],
+    #           "education": [
+    #             {
+    #               "degree": "[Degree]",
+    #               "institution": "[Institution]",
+    #               "location": "[Location]",
+    #               "graduationDate": "[Graduation Date]"
+    #             }
+    #           ],
+    #           "certifications": ["[Certification 1]", "[Certification 2]"] ,
+    #           "extraCurricular": "[Extra Curricular Activities]",
+    #           "achievment": "[Achievements]"
+    #         }
             
-            Instructions:
-            - Extract details accurately from the given resume.
-            - Ensure proper structuring of dates, responsibilities, and projects.
-            - If a field is missing in the input, leave it as an empty string or an empty list where applicable.
-            - Maintain proper formatting and avoid unnecessary additions.
+    #         Instructions:
+    #         - Extract details accurately from the given resume.
+    #         - Ensure proper structuring of dates, responsibilities, and projects.
+    #         - If a field is missing in the input, leave it as an empty string or an empty list where applicable.
+    #         - Maintain proper formatting and avoid unnecessary additions.
             
-            Provide the response in a valid JSON format with no additional explanations.
-            """
+    #         Provide the response in a valid JSON format with no additional explanations.
+    #         """
     output = llm.create_chat_completion(
         messages=[
             {"from": "user", "value": prompt + ' CV text: ' + cv_text},
+            # {"from": "user", "value": prompt + ' CV text: ' + cv_text},
         ],
         max_tokens=maxtokens,
         temperature=temperature
@@ -161,9 +162,9 @@ def pdf_to_text(prompt, maxtokens=2048, temperature=0, top_probability=0.95):
     # cv_text = ""
     # for page_id in page2content:
     #     cv_text += page2content[page_id] + ' '
-    converter = DocumentConverter()
-    result = converter.convert(cv_file)
-    cv_text = result.document.export_to_markdown()
+    # converter = DocumentConverter()
+    # result = converter.convert(cv_file)
+    # cv_text = result.document.export_to_markdown()
     
     llm = Llama(
         model_path="models/" + model_id,