diff --git a/.idea/vcs.xml b/.idea/vcs.xml
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+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
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diff --git a/Prompt_engineer/__pycache__/synthea_data.cpython-37.pyc b/Prompt_engineer/__pycache__/synthea_data.cpython-37.pyc
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diff --git a/Prompt_engineer/config.yaml b/Prompt_engineer/config.yaml
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index 0000000..a950330
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+++ b/Prompt_engineer/config.yaml
@@ -0,0 +1,12 @@
+# Configuration file for the medical report generator
+cleaned_data_csv_path: '/Users/ayodejioyesanya/Desktop/Tdata/cleaned_medical_data.csv'
+prompt_template: |
+  <s>[INST] <<SYS>> You are an experienced medical AI assistant trained to provide helpful and accurate 
+  information to patients. You have extensive knowledge of human anatomy, common medical conditions, and 
+  evidence-based treatments. Your responses should be empathetic, informative, and adhere to medical best 
+  practices. You will not provide any medical advice that could be harmful. [/INST]
+
+  Diagnosis: {diagnosis}. Patient's age: {age}. Gender: {gender}. 
+  Conditions: {conditions}. Observations: {observations}. Care plans: {care_plans}. 
+  Imaging modality: {modality}. Body area: {body_area}. 
+  Please provide a comprehensive report based on the patient's data and imaging results.</s>
diff --git a/Prompt_engineer/main.py b/Prompt_engineer/main.py
new file mode 100644
index 0000000..54f9c86
--- /dev/null
+++ b/Prompt_engineer/main.py
@@ -0,0 +1,82 @@
+import argparse
+import yaml
+import pandas as pd
+from datetime import datetime
+from typing import Dict, Any, Optional
+
+def load_config(config_path: str) -> Dict[str, Any]:
+    """
+    Load the YAML configuration file.
+    """
+    with open(config_path, 'r') as file:
+        config = yaml.safe_load(file) or {}
+    return config
+
+def calculate_age(birthdate: str) -> int:
+    """
+    Calculate age given the birthdate.
+    """
+    birth_date = datetime.strptime(birthdate, "%Y-%m-%d")
+    today = datetime.now()
+    return today.year - birth_date.year - ((today.month, today.day) < (birth_date.month, birth_date.day))
+
+def generate_medical_prompt(patient_data: Dict[str, Any], diagnosis: str, config: Dict[str, Any]) -> str:
+    """
+    Generate a medical prompt for the medical report based on patient data and diagnosis details using a template from the configuration.
+    Uses REASONDESCRIPTION as the source for the patient's conditions.
+    """
+    prompt_template = config.get('prompt_template', "Default template if not specified in config.")
+    age = calculate_age(patient_data['BIRTHDATE'])  # Calculating age using the BIRTHDATE field
+    conditions = patient_data.get('REASONDESCRIPTION', 'No reason description provided')  # Default message if not specified
+    prompt = prompt_template.format(
+        diagnosis=diagnosis,
+        age=age,
+        gender=patient_data['GENDER'],
+        conditions=conditions,  # Correct field used
+        observations=patient_data.get('observation', 'No observations recorded'),  # Use correct field for observations
+        care_plans=patient_data.get('DESCRIPTION_careplan', 'No care plans recorded'),
+        modality=patient_data.get('modality', "Not specified"),
+        body_area=patient_data.get('body_area', "Not specified")
+    )
+    return prompt
+
+def load_patient_data(csv_path: str) -> pd.DataFrame:
+    """
+    Load patient data from a CSV file.
+    """
+    return pd.read_csv(csv_path)
+
+def select_random_patient_data(patient_data: pd.DataFrame) -> Dict[str, Any]:
+    """
+    Select random patient data from the entire dataset, independent of the diagnosis.
+    """
+    return patient_data.sample(n=1).iloc[0].to_dict()
+
+def parse_arguments() -> argparse.Namespace:
+    """
+    Parse command line arguments.
+    """
+    parser = argparse.ArgumentParser(description='Medical Report Generator')
+    parser.add_argument('--config_path', type=str, default='/Users/ayodejioyesanya/Documents/SFdev/Prompt_engineer/config.yaml', help='Path to the YAML configuration file.')
+    parser.add_argument('--csv_path', type=str, default='/Users/ayodejioyesanya/Desktop/Tdata/cleaned_medical_data.csv', help='Path to the CSV file containing patient data.')
+    parser.add_argument('--diagnosis', type=str, required=True, help='Diagnosis determined by the image classifier.')
+    return parser.parse_args()
+
+def main() -> None:
+    """
+    Main function to orchestrate the workflow.
+    """
+    args = parse_arguments()
+    config = load_config(args.config_path)
+    patient_data = load_patient_data(args.csv_path)
+    diagnosis = args.diagnosis  # Diagnosis passed from the command line
+    random_patient_data = select_random_patient_data(patient_data)
+
+    if random_patient_data:
+        medical_prompt = generate_medical_prompt(random_patient_data, diagnosis, config)
+        print(medical_prompt)
+    else:
+        print("No patient data available.")
+
+if __name__ == '__main__':
+    main()
diff --git a/Prompt_engineer/synthea_data.py b/Prompt_engineer/synthea_data.py
new file mode 100644
index 0000000..b591636
--- /dev/null
+++ b/Prompt_engineer/synthea_data.py
@@ -0,0 +1,66 @@
+from typing import Dict, Any, Tuple, Optional
+from datetime import datetime
+from dateutil.relativedelta import relativedelta
+import random
+
+# Example static data, simulating a patient database
+PATIENT_DATA = [
+    {
+        'BIRTHDATE': '1990-01-01',
+        'GENDER': 'Male',
+        'REASONDESCRIPTION': 'lung cancer',
+        'observation': 'increased cough',
+        'DESCRIPTION_careplan': 'regular monitoring',
+        'modality': 'X-Ray',
+        'body_area': 'Chest'
+    },
+    {
+        'BIRTHDATE': '1985-05-15',
+        'GENDER': 'Female',
+        'REASONDESCRIPTION': '',  # This patient has no diagnosis specified.
+        'observation': 'shortness of breath',
+        'DESCRIPTION_careplan': 'oxygen therapy',
+        'modality': 'CT Scan',
+        'body_area': 'Chest'
+    },
+    # Additional records can be added here.
+]
+
+class SyntheaData:
+    """
+    Simulates data retrieval from a static dataset.
+    """
+    def get_patient_data_by_diagnosis(self, diagnosis: str) -> Tuple[Dict[str, Any], Optional[str], Optional[str]]:
+        """
+        Retrieves a random patient's data who has been diagnosed with a specified condition using the REASONDESCRIPTION.
+        If no diagnosis is provided in REASONDESCRIPTION, it defaults to "Nil significant past medical history."
+        """
+        # Filtering patients based on diagnosis, considering those with empty or null diagnosis as having no significant history.
+        matching_patients = [patient for patient in PATIENT_DATA if patient['REASONDESCRIPTION'].strip().lower() == diagnosis.lower()] if diagnosis.strip() else [patient for patient in PATIENT_DATA if not patient['REASONDESCRIPTION'].strip()]
+
+        if matching_patients:
+            selected_patient = random.choice(matching_patients)
+            age = relativedelta(datetime.now(), datetime.strptime(selected_patient['BIRTHDATE'], '%Y-%m-%d')).years
+            patient_data = {
+                'age': age,
+                'gender': selected_patient['GENDER'],
+                'conditions': selected_patient['REASONDESCRIPTION'] if selected_patient['REASONDESCRIPTION'].strip() else "Nil significant past medical history",
+                'observations': selected_patient['observation'],
+                'care_plans': selected_patient['DESCRIPTION_careplan'],
+                'modality': selected_patient.get('modality', "Not specified"),
+                'body_area': selected_patient.get('body_area', "Not specified")
+            }
+            modality = selected_patient.get('modality')
+            body_area = selected_patient.get('body_area')
+            return patient_data, modality, body_area
+        else:
+            # Default case when no patients match the criteria, including empty diagnosis search
+            return ({
+                'age': None,
+                'gender': None,
+                'conditions': "Nil significant past medical history",
+                'observations': None,
+                'care_plans': None,
+                'modality': None,
+                'body_area': None
+            }, None, None)
diff --git a/config.yaml b/config.yaml
deleted file mode 100644
index c28a9ca..0000000
--- a/config.yaml
+++ /dev/null
@@ -1,2 +0,0 @@
-database_path: 'path/to/health_data.db'
-cleaned_data_csv_path: '/path/to/cleaned_medical_data.csv'
diff --git a/main.py b/main.py
deleted file mode 100644
index 8288db7..0000000
--- a/main.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import argparse
-import yaml
-from typing import Dict, Any, Optional
-from transformers import AutoTokenizer, pipeline
-from synthea_data import SyntheaData  #SyntheaData class is in synthea_data.py
-
-def load_config(config_path: str) -> Dict[str, Any]:
-    """
-    Load the YAML configuration file.
-    """
-    with open(config_path, 'r') as file:
-        config = yaml.safe_load(file)
-    return config
-
-def generate_medical_prompt(patient_data: Dict[str, Any], diagnosis: str, modality: Optional[str], body_area: Optional[str]) -> str:
-    """
-    Generate a medical prompt for the LLAMA model based on patient data, a given diagnosis, and imaging details.
-    """
-    prompt_template = (
-        "Diagnosis: {diagnosis}. Patient's age: {age}. Gender: {gender}. "
-        "Conditions: {conditions}. Observations: {observations}. Care plans: {care_plans}. "
-        "Imaging modality: {modality}. Body area: {body_area}. "
-        "Please provide a comprehensive report based on the patient's data and imaging results."
-    )
-
-    prompt = prompt_template.format(
-        diagnosis=diagnosis,
-        age=patient_data['age'],
-        gender=patient_data['gender'],
-        conditions=', '.join(patient_data['conditions']),
-        observations=', '.join(patient_data['observations']),
-        care_plans=', '.join(patient_data['care_plans']),
-        modality=modality or "Not specified",
-        body_area=body_area or "Not specified"
-    )
-    return prompt
-
-def parse_arguments() -> argparse.Namespace:
-    """
-    Parse command line arguments.
-    """
-    parser = argparse.ArgumentParser(description='Medical Report Generator')
-    parser.add_argument('--config_path', type=str, default='config.yaml', help='Path to the YAML configuration file.')
-    return parser.parse_args()
-
-def main() -> None:
-    """
-    Main function to orchestrate the workflow.
-    """
-    args = parse_arguments()
-    config = load_config(args.config_path)
-
-    synthea_data = SyntheaData(config)
-    diagnosis = "lung cancer"  # Example diagnosis
-    patient_data, modality, body_area = synthea_data.get_patient_data_by_diagnosis(diagnosis)
-
-    if patient_data:
-        medical_prompt = generate_medical_prompt(patient_data, diagnosis, modality, body_area)
-        print(medical_prompt)
-        # Further processing with LLAMA or other models can be done here
-    else:
-        print("No patient data found for the given diagnosis.")
-
-if __name__ == '__main__':
-    main()
diff --git a/synthea_data.py b/synthea_data.py
deleted file mode 100644
index 34e32e8..0000000
--- a/synthea_data.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import pandas as pd
-import sqlite3
-from datetime import datetime
-from dateutil.relativedelta import relativedelta
-from typing import Dict, Any, Tuple
-import random
-
-class SyntheaData:
-    """
-    Handles operations related to processing and retrieving patient data from a consolidated dataset.
-    """
-    def __init__(self, config: Dict[str, Any]) -> None:
-        """
-        Initializes with configuration settings.
-        """
-        self.config = config
-
-    def import_cleaned_data_to_sqlite(self) -> None:
-        """
-        Imports cleaned medical data from a CSV file into an SQLite database.
-        """
-        conn = sqlite3.connect(self.config['database_path'])
-        df = pd.read_csv(self.config['cleaned_data_csv_path'])
-        df.to_sql('cleaned_medical_data', conn, if_exists='replace', index=False)
-        conn.close()
-
-    def get_patient_data_by_diagnosis(self, diagnosis: str) -> Tuple[Dict[str, Any], Optional[str], Optional[str]]:
-        """
-        Retrieves a random patient's data who has been diagnosed with a specified condition.
-        """
-        conn = sqlite3.connect(self.config['database_path'])
-        query = """
-        SELECT * FROM cleaned_medical_data
-        WHERE description_cond LIKE ? OR reasondescription LIKE ?
-        """
-        df = pd.read_sql_query(query, conn, params=(f'%{diagnosis}%', f'%{diagnosis}%'))
-        conn.close()
-
-        if not df.empty:
-            selected_row = df.sample(n=1).iloc[0]
-            patient_data = {
-                'age': relativedelta(datetime.now(), datetime.strptime(selected_row['BIRTHDATE'], '%Y-%m-%d')).years,
-                'gender': selected_row['GENDER'],
-                'conditions': [selected_row['description_cond']],
-                'observations': [selected_row.get('observation', '')],
-                'care_plans': [selected_row.get('DESCRIPTION_careplan', '')],
-            }
-            modality = selected_row.get('modality', None)
-            body_area = selected_row.get('body_area', None)
-            return patient_data, modality, body_area
-        else:
-            return {}, None, None