--- a
+++ b/main.py
@@ -0,0 +1,204 @@
+import sys
+import json
+import requests
+import openai
+from utils import get_values
+from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QLineEdit, QTextEdit, QPushButton, QVBoxLayout, QHBoxLayout, QWidget, QMessageBox, QListWidget
+from PyQt5.QtGui import QPixmap, QFont
+from PyQt5.QtCore import Qt
+
+# OpenAI Key
+openai.api_key = "OPENAI_API_KEY"
+
+with open("schema.txt", "r") as schema_file:
+    schema_prompt = schema_file.read()
+
+prime_query_prompt = "query top_n_associated_diseases {\n  search(queryString:"
+
+
+class GeneticsGPTGUI(QMainWindow):
+    def __init__(self):
+        super().__init__()
+        self.setWindowTitle("GeneticsGPT")
+        self.setGeometry(100, 100, 800, 600)
+
+        # main widget and layout
+        central_widget = QWidget(self)
+        main_layout = QHBoxLayout(central_widget)
+        main_layout.setContentsMargins(20, 20, 20, 20)
+        main_layout.setSpacing(20)
+        self.setCentralWidget(central_widget)
+
+        # left section layout
+        left_layout = QVBoxLayout()
+        left_layout.setSpacing(20)
+        main_layout.addLayout(left_layout)
+
+        # logo label
+        logo_label = QLabel(self)
+        logo_pixmap = QPixmap("logo.png")  # Replace with your logo image file
+        logo_pixmap = logo_pixmap.scaledToWidth(
+            200)  # Adjust the logo size as needed
+        logo_label.setPixmap(logo_pixmap)
+        logo_label.setAlignment(Qt.AlignCenter)
+        left_layout.addWidget(logo_label)
+
+        # question input field
+        self.question_input_field = QLineEdit(self)
+        self.question_input_field.setPlaceholderText("Ask a question...")
+        self.question_input_field.setFont(QFont("Arial", 14))
+        self.question_input_field.setStyleSheet(
+            "padding: 10px; border-radius: 5px;")
+        left_layout.addWidget(self.question_input_field)
+
+        # submit button
+        self.submit_button = QPushButton("Submit", self)
+        self.submit_button.clicked.connect(self.handle_submit)
+        self.submit_button.setFont(QFont("Arial", 14))
+        self.submit_button.setStyleSheet(
+            "padding: 10px; background-color: #4285F4; color: white; border-radius: 5px;")
+        left_layout.addWidget(self.submit_button)
+
+        # answer widget
+        self.answer_widget = QTextEdit(self)
+        self.answer_widget.setReadOnly(True)
+        self.answer_widget.setFont(QFont("Arial", 12))
+        self.answer_widget.setStyleSheet(
+            "background-color: #f0f0f0; color: black; padding: 10px; border-radius: 5px;")
+        left_layout.addWidget(QLabel("Answers:"))
+        left_layout.addWidget(self.answer_widget)
+
+        # FAQ list
+        self.faq_list = QListWidget(self)
+        self.faq_list.itemClicked.connect(self.handle_faq_click)
+        left_layout.addWidget(QLabel("Frequently Asked Questions:"))
+        left_layout.addWidget(self.faq_list)
+
+        # answer text area
+        self.answer_text_area = QTextEdit(self)
+        self.answer_text_area.setReadOnly(True)
+        self.answer_text_area.setFont(QFont("Arial", 12))
+        self.answer_text_area.setStyleSheet(
+            "background-color: #f0f0f0; color: black; padding: 10px; border-radius: 5px;")
+        main_layout.addWidget(self.answer_text_area)
+
+    def handle_submit(self):
+        user_question = self.question_input_field.text()
+
+        # prevent multiple requests
+        self.submit_button.setEnabled(False)
+
+        query_response = self.generate_query_response(user_question)
+
+        self.answer_widget.setPlainText(query_response)
+
+        # Generate FAQs based on the query response
+        self.generate_faqs(query_response)
+
+        self.submit_button.setEnabled(True)
+
+    def handle_faq_click(self, item):
+        faq_question = item.text()
+
+        # Generate a response for the clicked FAQ
+        self.generate_faq_response(faq_question)
+
+    def generate_query_response(self, user_question):
+        openai_response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": schema_prompt},
+                {"role": "user", "content": user_question},
+                {"role": "system", "content": prime_query_prompt},
+            ],
+            temperature=0,
+            max_tokens=250,
+            top_p=1,
+            frequency_penalty=0,
+            presence_penalty=0,
+            stop=["###"],
+        )
+        generated_query = openai_response["choices"][0].message["content"]
+
+        graphql_query = prime_query_prompt + generated_query
+
+        # Set base URL of GraphQL API endpoint
+        api_url = "https://api.platform.opentargets.org/api/v4/graphql"
+
+        # Perform POST request and check status code of response
+        try:
+            api_response = requests.post(
+                api_url, json={"query": graphql_query})
+            api_response.raise_for_status()
+        except requests.exceptions.HTTPError as err:
+            print(err)
+            QMessageBox.critical(
+                self, "Error", "An error occurred while fetching data from the API.")
+            return None
+
+        # Transform API response from JSON into Python dictionary
+        api_data = json.loads(api_response.text)
+
+        try:
+            search_hits = api_data["data"]["search"]["hits"][0]
+        except (KeyError, IndexError):
+            QMessageBox.warning(
+                self, "Warning", "No results found for the given query.")
+            return None
+
+        diseases = get_values(search_hits, "disease")
+        answer_text = "\n".join(
+            f"{i+1}. {disease['name']}" for i, disease in enumerate(diseases))
+
+        return answer_text
+
+    def generate_faqs(self, query_response):
+        if query_response:
+            faq_prompt = f"Based on the following information:\n\n{query_response}\n\nGenerate 3-5 relevant frequently asked questions (FAQs) related to the diseases and genes mentioned. Provide each FAQ as a question only, without the 'Q:' prefix or any additional context."
+
+            openai_response = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo",
+                messages=[
+                    {"role": "system", "content": schema_prompt},
+                    {"role": "user", "content": faq_prompt},
+                ],
+                temperature=0.7,
+                max_tokens=200,
+                top_p=1,
+                frequency_penalty=0,
+                presence_penalty=0,
+            )
+
+            generated_faqs = openai_response["choices"][0].message["content"]
+
+            self.faq_list.clear()
+
+            # Add generated FAQs to the list
+            for faq in generated_faqs.split("\n"):
+                self.faq_list.addItem(faq)
+
+    def generate_faq_response(self, faq_question):
+        faq_prompt = f"Q: {faq_question}\nA: Provide a concise and informative answer to the question, focusing on the key points and avoiding unnecessary details or technical jargon."
+
+        openai_response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": schema_prompt},
+                {"role": "user", "content": faq_prompt},
+            ],
+            temperature=0.7,
+            max_tokens=200,
+            top_p=1,
+            frequency_penalty=0,
+            presence_penalty=0,
+        )
+
+        faq_response = openai_response["choices"][0].message["content"]
+        self.answer_text_area.setPlainText(faq_response)
+
+
+if __name__ == "__main__":
+    app = QApplication(sys.argv)
+    gui = GeneticsGPTGUI()
+    gui.show()
+    sys.exit(app.exec_())