first_sync_test

README.md

@@ -0,0 +1,78 @@
+---
+title: Llm Bill Chat
+emoji: 🥸🧮
+colorFrom: indigo
+colorTo: red
+sdk: streamlit
+sdk_version: 1.41.1
+app_file: bill.py
+pinned: false
+license: apache-2.0
+short_description: 'LLM app '
+---
+
+# LLM Bill Chat App
+
+This project is a proof of concept for a chat application utilizing a Large Language Model (LLM) to assist users with their telecom billing inquiries. The application is built using Python and Streamlit, providing an interactive web interface for users to engage with.
+
+## Features
+
+- Maintain chat conversation context
+- Allow users to query their billing information
+- Compare the last four bills and provide insights
+- Respond exclusively with the user's own billing information
+- Save user information and conversation history
+
+## Project Structure
+
+```
+llm-bill-chat-app
+├── src
+│   ├── chat
+│   │   ├── __init__.py       # Package initialization for chat module
+│   │   ├── context.py         # Manages conversation context
+│   │   ├── bill_comparison.py  # Compares user bills
+│   │   ├── user_info.py       # Handles user-specific information
+│   │   └── conversation.py     # Manages conversation flow
+│   └── utils
+│       └── __init__.py       # Package 
+├── bill.py            # Main entry point for the Streamlit app
+initialization for utils module
+├── requirements.txt           # Project dependencies
+└── README.md                  # Project documentation
+```
+
+## Installation
+
+1. Clone the repository:
+   ```
+   git remote add origin https://github.com/serbantica/llm-bill-chat.git
+   cd llm-chat-app
+   ```
+
+2. Create and activate a virtual environment (Windows example):
+   ```
+   python -m venv .venv .venv\Scrips\activate
+   ```
+
+3. Install the required dependencies:
+   ```
+   pip install -r requirements.txt
+   ```
+
+## Usage
+
+To run the application, execute the following command:
+```
+streamlit run bill.py
+```
+
+Open your web browser and navigate to `http://localhost:8501` to interact with the chat application.
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or improvements.
+
+## License
+
+This project is licensed under the MIT License. See the LICENSE file for more details.

bill.py

@@ -0,0 +1,216 @@
+#!/usr/bin/env -S poetry run python
+
+import os
+import json
+import pdfplumber
+import streamlit as st
+from openai import OpenAI
+
+client = OpenAI()
+
+def load_user_data(user_id):
+    file_path = os.path.join("data", "user_data", f"user_data_{user_id}.json")
+    if not os.path.exists(file_path):
+        return {}
+    with open(file_path, "r") as file:
+        return json.load(file)
+
+def parse_pdf_to_json(pdf_path):
+    user_id = {}
+    serie_factura = {}
+    data_factura = {}
+    costuri = {}
+    with pdfplumber.open(pdf_path, ) as pdf:
+        for page in pdf.pages:
+            text = page.extract_text()
+            if text:
+                lines = text.split('\n')
+
+                # Process each line and look for specific categories
+                for line in lines:
+                    # Check for 'Data emiterii facturii'
+                    if 'Data facturii' in line:
+                        date = line.split()[-1]
+                        data_factura['Data factura'] = date
+
+                    # Check for 'Serie factură'
+                    if 'rul facturii:' in line:
+                        serie = line.split()[-1]
+                        serie_factura['Serie numar'] = serie
+                        
+                    # Check for 'Cont client'
+                    if 'Cont client' in line:
+                        cont = line.split()[-1]
+                        user_id['Cont client'] = cont
+                    
+                    # Check for 'Valoare facturată fără TVA'
+                    if 'Sold precedent' in line:
+                        value = line.split()[-2].replace(',', '.')  # Extract and convert to float
+                        costuri['Sold precedent'] = value
+                    
+                    # Check for 'Total bază de impozitare TVA'
+                    elif 'din sold precedent' in line:
+                        value = line.split()[-2].replace(',', '.')  # Extract and convert to float
+                        costuri['Total platit din sold precedent'] = value
+                    
+                    # Check for 'TVA'
+                    elif 'TVA' in line and '%' in line:
+                        value = line.split()[-2].replace(',', '.')  # Extract and convert to float
+                        costuri['TVA'] = value
+                    
+                    # Check for 'Dobânzi penalizatoare'
+                    elif 'Abonamente' in line:
+                        value = line.split()[-2].replace(',', '.')  # Extract and convert to float
+                        costuri['Abonamente si extraopiuni'] = value
+                    
+                    # Check for 'TOTAL DE PLATĂ FACTURĂ CURENTĂ'
+                    elif 'Total factura curenta fara TVA' in line:
+                        value = float(line.split()[-2].replace(',', '.'))  # Extract and convert to float
+                        costuri['Total factura curenta fara TVA'] = value
+                    
+                    # Check for 'Sold Cont Contract'
+                    elif 'Servicii utilizate' in line:
+                        value = line.split()[-2].replace(',', '.')  # Extract and convert to float
+                        costuri['Servicii utilizate'] = value
+                    
+                    # Check for 'Compensatii'
+                    elif 'Rate terminal' in line:
+                        value = float(line.split()[-2].replace(',', '.'))  # Extract and convert to float
+                        costuri['Rate terminal'] = value
+
+                    # Check for 'TVA 19,00%'
+                    elif 'TVA 19,00%' in line:
+                        value = float(line.split()[-2].replace(',', '.'))  # Extract and convert to float
+                        costuri['TVA'] = value
+
+                    # Check for 'Compensatii'
+                    elif 'Total factura curenta' in line:
+                        value = float(line.split()[-2].replace(',', '.'))  # Extract and convert to float
+                        costuri['Total factura curenta'] = value
+
+    return costuri
+
+def check_related_keys(question, user_id):
+    user_data = load_user_data(user_id)
+    bill_keys = set()
+    for bill in user_data.get("bills", []):
+        bill_keys.update(bill.keys())
+    return [key for key in bill_keys if key.lower() in question.lower()]
+
+def process_query(query, user_id):
+    user_data = load_user_data(user_id)
+    bill_info = user_data.get("bills", [])
+    related_keys = check_related_keys(query, user_id)
+    related_keys_str = ", ".join(related_keys) if related_keys else "N/A"
+
+    if related_keys_str != "N/A":
+        context = (
+            f"Citeste informatiile despre costrurile in lei facturate din dictionar: {bill_info} "
+            f"si raspunde la intrebarea: '{query}' dar numai cu info legate de: {related_keys_str}"
+        )
+    else:
+        context = (
+            f"Citeste informatiile despre costrurile in lei facturate din dictionar: {bill_info} "
+            f"si raspunde la intrebarea: '{query}' dar numai cu info legate de factura"
+        )
+
+    max_input_length = 550
+    st.write(f"Context:\n{context}")
+    st.write(f"Context size: {len(context)} characters")
+
+    if len(context) > max_input_length:
+        st.warning("Prea multe caractere în context, solicitarea nu va fi trimisă.")
+        return None
+
+    return context
+
+def main():
+
+    st.title("Telecom Bill Chat with LLM Agent")
+
+    if "user_id" not in st.session_state:
+        st.session_state.user_id = None
+
+    user_id = st.sidebar.text_input("Introdu numărul de telefon:")
+    if user_id and user_id != st.session_state.user_id:
+        data = load_user_data(user_id)
+        if data:
+            st.session_state.user_id = user_id
+            st.success("Utilizator găsit!")
+        else:
+            st.warning("Nu am găsit date pentru acest ID. Încărcați o factură PDF la nevoie.")
+            st.session_state.user_id = user_id
+
+    uploaded_file = st.file_uploader("Încarcă factura PDF", type="pdf")
+    if uploaded_file and st.session_state.user_id:
+        bill_data = parse_pdf_to_json(uploaded_file)
+        existing_data = load_user_data(st.session_state.user_id)
+        if "bills" not in existing_data:
+            existing_data["bills"] = []
+        existing_data["bills"].append(bill_data)
+        file_path = os.path.join("data", "user_data", f"user_data_{st.session_state['user_id']}.json")
+        os.makedirs(os.path.dirname(file_path), exist_ok=True)
+        with open(file_path, "w") as file:
+            json.dump(existing_data, file)
+        st.success("Factura a fost încărcată și salvată cu succes!")
+
+    if st.session_state.user_id:
+        data = load_user_data(st.session_state.user_id)
+        st.write(f"Phone Number: {st.session_state.user_id}")
+        st.write("Facturi existente:")
+        for bill in data.get("bills", []):
+            st.write(bill)
+    else:
+        st.info("Introduceți un ID și/sau încărcați o factură PDF pentru a continua.")
+
+    # Initialize conversation in the session state
+    # "context_prompt_added" indicates whether we've added the specialized "bill info" context yet.
+    if "messages" not in st.session_state:
+        st.session_state["messages"] = [
+            {"role": "assistant", "content": "Cu ce te pot ajuta?"}
+        ]
+    if "context_prompt_added" not in st.session_state:
+        st.session_state.context_prompt_added = False
+
+    st.write("---")
+    st.subheader("Chat")
+
+    for msg in st.session_state["messages"]:
+        st.chat_message(msg["role"]).write(msg["content"])
+
+    if prompt := st.chat_input("Introduceți întrebarea aici:"):
+        if not st.session_state.user_id:
+            st.error("Trebuie să introduceți un număr de telefon valid sau să încărcați date.")
+            return
+
+        # If the context prompt hasn't been added yet, build & inject it once;
+        # otherwise, just add the user's raw question.
+        if not st.session_state.context_prompt_added:
+            final_prompt = process_query(prompt, st.session_state["user_id"])
+            if final_prompt is None:
+                st.stop()
+            st.session_state["messages"].append({"role": "user", "content": final_prompt})
+            st.session_state.context_prompt_added = True
+        else:
+            st.session_state["messages"].append({"role": "user", "content": prompt})
+
+        # Display the latest user message in the chat
+        st.chat_message("user").write(st.session_state["messages"][-1]["content"])
+
+        # Now call GPT-4 with the entire conversation
+        completion = client.chat.completions.create(
+            model="gpt-4",
+            messages=st.session_state["messages"]
+        )
+        response_text = completion.choices[0].message.content.strip()
+
+        st.session_state["messages"].append({"role": "assistant", "content": response_text})
+        st.chat_message("assistant").write(response_text)
+
+        if hasattr(completion, "usage"):
+            st.write("Prompt tokens:", completion.usage.prompt_tokens)
+            st.write("Completion tokens:", completion.usage.completion_tokens)
+            st.write("Total tokens:", completion.usage.total_tokens)
+
+if __name__ == "__main__":
+    main()