Upload 2 files

app.py

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+import streamlit as st
+from bot import supertec_bot
+
+
+didx_chatbot = supertec_bot()
+
+st.title("SuperTech Bot")
+user_input = st.text_input('How can I help you?')
+with st.spinner('Sit back and relax. It takes a while.'):
+    if st.button('Ask'):
+        if user_input:
+            answer = didx_chatbot.user_chat(user_input)
+            st.write(answer)
+
+
+
+
+
+
+

bot.py

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+class supertec_bot:
+    def __init__(self):
+        import openai
+
+
+
+        def get_data():
+            import requests
+            headers = {
+                'User-Agent': 'Mozilla/5.0',  # You can try different User-Agent strings if needed
+            }
+
+            payload = {'company_token': 'II@tNfQ70O'}
+
+            response = requests.post("https://superteclabs.com/apis2/retrieveallusers.php", data=payload,
+                                     headers=headers)
+
+            data = response.json()
+            return response.text
+
+        self.get_data = get_data
+
+        function_balance = {
+            "type": "function",
+            "function": {
+                "name": "get_data",
+                "description": "Retrieve the data from this ",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "query": {
+                            "type": "string",
+                            "description": "show me the all data "
+                        },
+                    },
+                    "required": ["query"]
+                }
+            }
+        }
+
+        import os
+        from dotenv import load_dotenv
+
+        # Load environment variables from .env file
+        load_dotenv()
+
+        self.client = openai.OpenAI(api_key=os.environ['openai_api_key'])
+        print(self.client)
+        # Step 1: Create an Assistant
+        # self.assistant = self.client.beta.assistants.create(
+        #     name="library Support Chatbot",
+        #     instructions="You are a personal Supertech support chatbot.",
+        #     tools=[function_balance],
+        #     model="gpt-3.5-turbo",
+        # )
+
+
+
+
+    def user_chat(self,query):
+        import time
+        # Step 2: Create a Thread
+        thread = self.client.beta.threads.create()
+
+        # Step 3: Add a Message to a Thread
+        message = self.client.beta.threads.messages.create(
+            thread_id=thread.id,
+            role="user",
+            content=query
+        )
+
+        # Step 4: Run the Assistant
+        run = self.client.beta.threads.runs.create(
+            thread_id=thread.id,
+            assistant_id="asst_tbdEcvmCNA4uFRPcxyVt8KNR",
+            instructions=""
+        )
+        answer = None
+        while True:
+            # Retrieve the run status
+            run_status = self.client.beta.threads.runs.retrieve(
+                thread_id=thread.id,
+                run_id=run.id
+            )
+            # print(run_status.model_dump_json(indent=4))
+            run_status.model_dump_json(indent=4)
+
+            # If run is completed, get messages
+            if run_status.status == 'completed':
+                messages = self.client.beta.threads.messages.list(
+                    thread_id=thread.id
+                )
+                # Loop through messages and print content based on role
+                for msg in messages.data:
+                    role = msg.role
+                    content = msg.content[0].text.value
+                    print(f"{role.capitalize()}: {content}")
+                    answer = f"{role.capitalize()}: {content}"
+                    break
+                break
+            elif run_status.status == 'requires_action':
+                # print("Function Calling")
+                required_actions = run_status.required_action.submit_tool_outputs.model_dump()
+                # print('required action test: ',required_actions)
+                tool_outputs = []
+                import json
+                for action in required_actions["tool_calls"]:
+                    func_name = action['function']['name']
+                    arguments = json.loads(action['function']['arguments'])
+
+                    if func_name == "get_data":
+                        output = self.get_data()
+                        tool_outputs.append({
+                            "tool_call_id": action['id'],
+                            "output": output
+                        })
+
+                    else:
+                        raise ValueError(f"Unknown function: {func_name}")
+
+                print("Submitting outputs back to the Assistant...")
+                self.client.beta.threads.runs.submit_tool_outputs(
+                    thread_id=thread.id,
+                    run_id=run.id,
+                    tool_outputs=tool_outputs
+                )
+            else:
+                print("Waiting for the Assistant to process...")
+                time.sleep(5)
+
+        if answer is not None:
+            print(f'this is my answer : ', answer)
+            return answer