app.py

import json
import requests
import streamlit as st
import pdfplumber
import pandas as pd
import sqlalchemy
from typing import Any, Dict, List

# Provider clients – ensure these libraries are installed
try:
    from openai import OpenAI
except ImportError:
    OpenAI = None

try:
    import groq
except ImportError:
    groq = None

# Hugging Face inference endpoint
HF_API_URL = "https://api-inference.huggingface.co/models/"
DEFAULT_TEMPERATURE = 0.1
GROQ_MODEL = "mixtral-8x7b-32768"


class QADataGenerator:
    """
    A Q&A Synthetic Generator that extracts and generates question-answer pairs
    from various input sources using an LLM provider.
    """
    def __init__(self) -> None:
        self._setup_providers()
        self._setup_input_handlers()
        self._initialize_session_state()
        # This prompt instructs the LLM to generate three Q&A pairs.
        self.custom_prompt_template = (
            "You are an expert in extracting question and answer pairs from documents. "
            "Generate 3 Q&A pairs from the following data, formatted as a JSON list of dictionaries. "
            "Each dictionary must have keys 'question' and 'answer'. "
            "The questions should be clear and concise, and the answers must be based solely on the provided data with no external information. "
            "Do not hallucinate. \n\n"
            "Example JSON Output:\n"
            "[{'question': 'What is the capital of France?', 'answer': 'Paris'}, "
            "{'question': 'What is the highest mountain in the world?', 'answer': 'Mount Everest'}, "
            "{'question': 'What is the chemical symbol for gold?', 'answer': 'Au'}]\n\n"
            "Now, generate 3 Q&A pairs from this data:\n{data}"
        )
    
    def _setup_providers(self) -> None:
        """Configure available LLM providers and their client initialization routines."""
        self.providers: Dict[str, Dict[str, Any]] = {
            "Deepseek": {
                "client": lambda key: OpenAI(base_url="https://api.deepseek.com/v1", api_key=key) if OpenAI else None,
                "models": ["deepseek-chat"],
            },
            "OpenAI": {
                "client": lambda key: OpenAI(api_key=key) if OpenAI else None,
                "models": ["gpt-4-turbo", "gpt-3.5-turbo"],
            },
            "Groq": {
                "client": lambda key: groq.Groq(api_key=key) if groq else None,
                "models": [GROQ_MODEL],
            },
            "HuggingFace": {
                "client": lambda key: {"headers": {"Authorization": f"Bearer {key}"}},
                "models": ["gpt2", "llama-2"],
            },
        }
    
    def _setup_input_handlers(self) -> None:
        """Register handlers for different input data types."""
        self.input_handlers: Dict[str, Any] = {
            "text": self.handle_text,
            "pdf": self.handle_pdf,
            "csv": self.handle_csv,
            "api": self.handle_api,
            "db": self.handle_db,
        }
    
    def _initialize_session_state(self) -> None:
        """Initialize Streamlit session state with default configuration."""
        defaults = {
            "config": {
                "provider": "OpenAI",
                "model": "gpt-4-turbo",
                "temperature": DEFAULT_TEMPERATURE,
            },
            "api_key": "",
            "inputs": [],       # List to store input sources
            "qa_pairs": "",     # Generated Q&A pairs output
            "error_logs": [],   # To store any error messages
        }
        for key, value in defaults.items():
            if key not in st.session_state:
                st.session_state[key] = value
    
    def log_error(self, message: str) -> None:
        """Log an error message to session state and display it."""
        st.session_state.error_logs.append(message)
        st.error(message)
    
    # ----- Input Handlers -----
    def handle_text(self, text: str) -> Dict[str, Any]:
        return {"data": text, "source": "text"}
    
    def handle_pdf(self, file) -> Dict[str, Any]:
        try:
            with pdfplumber.open(file) as pdf:
                full_text = ""
                for page in pdf.pages:
                    page_text = page.extract_text() or ""
                    full_text += page_text + "\n"
                return {"data": full_text, "source": "pdf"}
        except Exception as e:
            self.log_error(f"PDF Processing Error: {e}")
            return {"data": "", "source": "pdf"}
    
    def handle_csv(self, file) -> Dict[str, Any]:
        try:
            df = pd.read_csv(file)
            # Convert the DataFrame to a JSON string
            return {"data": df.to_json(orient="records"), "source": "csv"}
        except Exception as e:
            self.log_error(f"CSV Processing Error: {e}")
            return {"data": "", "source": "csv"}
    
    def handle_api(self, config: Dict[str, str]) -> Dict[str, Any]:
        try:
            response = requests.get(config["url"], headers=config.get("headers", {}), timeout=10)
            response.raise_for_status()
            return {"data": json.dumps(response.json()), "source": "api"}
        except Exception as e:
            self.log_error(f"API Processing Error: {e}")
            return {"data": "", "source": "api"}
    
    def handle_db(self, config: Dict[str, str]) -> Dict[str, Any]:
        try:
            engine = sqlalchemy.create_engine(config["connection"])
            with engine.connect() as conn:
                result = conn.execute(sqlalchemy.text(config["query"]))
                rows = [dict(row) for row in result]
            return {"data": json.dumps(rows), "source": "db"}
        except Exception as e:
            self.log_error(f"Database Processing Error: {e}")
            return {"data": "", "source": "db"}
    
    def aggregate_inputs(self) -> str:
        """Combine all input sources into a single aggregated string."""
        aggregated_data = ""
        for item in st.session_state.inputs:
            aggregated_data += f"Source: {item.get('source', 'unknown')}\n"
            aggregated_data += item.get("data", "") + "\n\n"
        return aggregated_data.strip()
    
    def build_prompt(self) -> str:
        """
        Build the complete prompt using the custom template and aggregated inputs.
        """
        data = self.aggregate_inputs()
        prompt = self.custom_prompt_template.format(data=data)
        st.write("### Built Prompt")
        st.write(prompt)
        return prompt
    
    def generate_qa_pairs(self) -> bool:
        """
        Generate Q&A pairs by sending the built prompt to the selected LLM provider.
        """
        api_key = st.session_state.api_key
        if not api_key:
            self.log_error("API key is missing!")
            return False
        
        provider_name = st.session_state.config["provider"]
        provider_cfg = self.providers.get(provider_name)
        if not provider_cfg:
            self.log_error(f"Provider {provider_name} is not configured.")
            return False
        
        client_initializer = provider_cfg["client"]
        client = client_initializer(api_key)
        model = st.session_state.config["model"]
        temperature = st.session_state.config["temperature"]
        prompt = self.build_prompt()
        
        st.info(f"Using **{provider_name}** with model **{model}** at temperature **{temperature:.2f}**")
        try:
            if provider_name == "HuggingFace":
                response = self._huggingface_inference(client, prompt, model)
            else:
                response = self._standard_inference(client, prompt, model, temperature)
            
            st.write("### Raw API Response")
            st.write(response)
            
            qa_pairs = self._parse_response(response, provider_name)
            st.write("### Parsed Q&A Pairs")
            st.write(qa_pairs)
            
            st.session_state.qa_pairs = qa_pairs
            return True
        except Exception as e:
            self.log_error(f"Generation failed: {e}")
            return False
    
    def _standard_inference(self, client: Any, prompt: str, model: str, temperature: float) -> Any:
        """Inference method for providers using an OpenAI-compatible API."""
        try:
            st.write("Sending prompt via standard inference...")
            result = client.chat.completions.create(
                model=model,
                messages=[{"role": "user", "content": prompt}],
                temperature=temperature,
            )
            st.write("Standard inference result received.")
            return result
        except Exception as e:
            self.log_error(f"Standard Inference Error: {e}")
            return None
    
    def _huggingface_inference(self, client: Dict[str, Any], prompt: str, model: str) -> Any:
        """Inference method for the Hugging Face Inference API."""
        try:
            st.write("Sending prompt to HuggingFace API...")
            response = requests.post(
                HF_API_URL + model,
                headers=client["headers"],
                json={"inputs": prompt},
                timeout=30,
            )
            response.raise_for_status()
            st.write("HuggingFace API response received.")
            return response.json()
        except Exception as e:
            self.log_error(f"HuggingFace Inference Error: {e}")
            return None
    
    def _parse_response(self, response: Any, provider: str) -> List[Dict[str, str]]:
        """
        Parse the LLM response and return a list of Q&A pairs.
        Expects the response to be JSON formatted.
        """
        st.write("Parsing response for provider:", provider)
        try:
            if provider == "HuggingFace":
                # For HuggingFace, assume the generated text is under "generated_text"
                if isinstance(response, list) and response and "generated_text" in response[0]:
                    raw_text = response[0]["generated_text"]
                else:
                    self.log_error("Unexpected HuggingFace response format.")
                    return []
            else:
                # For OpenAI (and similar providers) assume the response is similar to:
                # response.choices[0].message.content
                if response and hasattr(response, "choices") and response.choices:
                    raw_text = response.choices[0].message.content
                else:
                    self.log_error("Unexpected response format from provider.")
                    return []
            
            # Try parsing the raw text as JSON
            try:
                qa_list = json.loads(raw_text)
                if isinstance(qa_list, list):
                    return qa_list
                else:
                    self.log_error("Parsed output is not a list.")
                    return []
            except json.JSONDecodeError as e:
                self.log_error(f"JSON Parsing Error: {e}. Raw output: {raw_text}")
                return []
        except Exception as e:
            self.log_error(f"Response Parsing Error: {e}")
            return []


# ============ UI Components ============

def config_ui(generator: QADataGenerator):
    """Display configuration options in the sidebar."""
    with st.sidebar:
        st.header("Configuration")
        provider = st.selectbox("Select Provider", list(generator.providers.keys()))
        st.session_state.config["provider"] = provider
        provider_cfg = generator.providers[provider]
        
        model = st.selectbox("Select Model", provider_cfg["models"])
        st.session_state.config["model"] = model
        
        temperature = st.slider("Temperature", 0.0, 1.0, DEFAULT_TEMPERATURE)
        st.session_state.config["temperature"] = temperature
        
        api_key = st.text_input(f"{provider} API Key", type="password")
        st.session_state.api_key = api_key

def input_ui(generator: QADataGenerator):
    """Display input data source options using tabs."""
    st.subheader("Input Data Sources")
    tabs = st.tabs(["Text", "PDF", "CSV", "API", "Database"])
    
    with tabs[0]:
        text_input = st.text_area("Enter text input", height=150)
        if st.button("Add Text Input", key="text_input"):
            if text_input.strip():
                st.session_state.inputs.append(generator.handle_text(text_input))
                st.success("Text input added!")
            else:
                st.warning("Empty text input.")
    
    with tabs[1]:
        pdf_file = st.file_uploader("Upload PDF", type=["pdf"])
        if pdf_file is not None:
            st.session_state.inputs.append(generator.handle_pdf(pdf_file))
            st.success("PDF input added!")
    
    with tabs[2]:
        csv_file = st.file_uploader("Upload CSV", type=["csv"])
        if csv_file is not None:
            st.session_state.inputs.append(generator.handle_csv(csv_file))
            st.success("CSV input added!")
    
    with tabs[3]:
        api_url = st.text_input("API Endpoint URL")
        api_headers = st.text_area("API Headers (JSON format, optional)", height=100)
        if st.button("Add API Input", key="api_input"):
            headers = {}
            try:
                if api_headers:
                    headers = json.loads(api_headers)
            except Exception as e:
                generator.log_error(f"Invalid JSON for API Headers: {e}")
            st.session_state.inputs.append(generator.handle_api({"url": api_url, "headers": headers}))
            st.success("API input added!")
    
    with tabs[4]:
        db_conn = st.text_input("Database Connection String")
        db_query = st.text_area("Database Query", height=100)
        if st.button("Add Database Input", key="db_input"):
            st.session_state.inputs.append(generator.handle_db({"connection": db_conn, "query": db_query}))
            st.success("Database input added!")

def output_ui(generator: QADataGenerator):
    """Display the generated Q&A pairs and provide a download option."""
    st.subheader("Q&A Pairs Output")
    if st.session_state.qa_pairs:
        st.write("### Generated Q&A Pairs")
        st.write(st.session_state.qa_pairs)
        st.download_button(
            "Download Output",
            json.dumps(st.session_state.qa_pairs, indent=2),
            file_name="qa_pairs.json",
            mime="application/json"
        )
    else:
        st.info("No Q&A pairs generated yet.")

def logs_ui():
    """Display error logs and debugging information in an expandable section."""
    with st.expander("Error Logs & Debug Info", expanded=False):
        if st.session_state.error_logs:
            for log in st.session_state.error_logs:
                st.write(log)
        else:
            st.write("No logs yet.")


def main():
    st.set_page_config(page_title="Advanced Q&A Synthetic Generator", layout="wide")
    st.title("Advanced Q&A Synthetic Generator")
    st.markdown(
        """
        Welcome to the Advanced Q&A Synthetic Generator. This tool extracts and generates question-answer pairs
        from various input sources. Configure your provider in the sidebar, add input data, and click the button below to generate Q&A pairs.
        """
    )
    
    # Initialize generator and display configuration UI
    generator = QADataGenerator()
    config_ui(generator)
    
    st.header("1. Input Data")
    input_ui(generator)
    if st.button("Clear All Inputs"):
        st.session_state.inputs = []
        st.success("All inputs have been cleared!")
    
    st.header("2. Generate Q&A Pairs")
    if st.button("Generate Q&A Pairs", key="generate_qa"):
        with st.spinner("Generating Q&A pairs..."):
            if generator.generate_qa_pairs():
                st.success("Q&A pairs generated successfully!")
            else:
                st.error("Q&A generation failed. Check logs for details.")
    
    st.header("3. Output")
    output_ui(generator)
    
    st.header("4. Logs & Debug Information")
    logs_ui()


if __name__ == "__main__":
    main()