import streamlit as st
import pandas as pd
from transformers import AutoTokenizer, AutoModel
import torch
import graphrag
import inspect

# Diagnostic Section
st.title("Graphrag Module Investigation")

st.write("Contents of graphrag module:")
module_contents = dir(graphrag)
st.write(module_contents)

st.write("Detailed information about graphrag module contents:")
for item in module_contents:
    attr = getattr(graphrag, item)
    st.write(f"Name: {item}")
    st.write(f"Type: {type(attr)}")
    
    if inspect.isclass(attr):
        st.write("Class Methods and Attributes:")
        for name, value in inspect.getmembers(attr):
            if not name.startswith('_'):  # Exclude private methods/attributes
                st.write(f"  - {name}: {type(value)}")
    
    if callable(attr):
        st.write("Signature:")
        st.write(inspect.signature(attr))
        st.write("Docstring:")
        st.write(inspect.getdoc(attr))
    
    st.write("---")

# Main Application Section
st.title("Graphrag Text Analysis")

@st.cache_resource
def load_model():
    bert_model_name = "bert-base-uncased"
    tokenizer = AutoTokenizer.from_pretrained(bert_model_name)
    bert_model = AutoModel.from_pretrained(bert_model_name)

    # Initialize Graphrag model
    # Note: This part may need to be adjusted based on the actual structure of graphrag
    model = None
    for item in module_contents:
        if 'model' in item.lower() or 'rag' in item.lower():
            model_class = getattr(graphrag, item)
            if inspect.isclass(model_class):
                try:
                    # Attempt to initialize the model
                    # You may need to adjust the parameters based on the actual class signature
                    model = model_class(bert_model)
                    st.success(f"Successfully initialized {item}")
                    break
                except Exception as e:
                    st.write(f"Tried initializing {item}, but got error: {str(e)}")

    if model is None:
        st.error("Could not initialize any Graphrag model. Please check the module structure.")
    
    return tokenizer, model

def process_text(text, tokenizer, model):
    if model is None:
        return "Model not initialized"
    
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Process outputs based on your specific task
    # This is a placeholder; adjust according to your model's output
    if hasattr(outputs, 'logits'):
        logits = outputs.logits
    elif isinstance(outputs, torch.Tensor):
        logits = outputs
    else:
        return f"Unexpected output format: {type(outputs)}"
    
    probabilities = torch.softmax(logits, dim=1)
    return probabilities.tolist()[0]

tokenizer, model = load_model()

# Text input for single prediction
text_input = st.text_area("Enter text for analysis:")
if st.button("Analyze Text"):
    if text_input:
        result = process_text(text_input, tokenizer, model)
        st.write(f"Analysis Result: {result}")
    else:
        st.write("Please enter some text to analyze.")

# Note about sample data
st.markdown("Note: To use a CSV file, you would typically upload it and process each row. For simplicity, we're using direct text input in this example.")