import streamlit as st
import pandas as pd
import numpy as np
from transformers import pipeline
from geopy.geocoders import Nominatim

# Load Models
@st.cache_resource
def load_llama_small_model():
    model_name = "meta-llama/Llama-2-7b"
    generator = pipeline("text-generation", model=model_name)
    return generator

@st.cache_resource
def load_llama_large_model():
    model_name = "meta-llama/Llama-3.1-405b"
    generator = pipeline("text-generation", model=model_name)
    return generator

@st.cache_resource
def load_multimodal_model():
    model_name = "google/vit-base-patch16-224"
    vision_model = pipeline("image-classification", model=model_name)
    return vision_model

@st.cache_resource
def load_text_classification_model():
    model_name = "distilbert-base-uncased"
    text_classifier = pipeline("text-classification", model=model_name)
    return text_classifier

def main():
    st.title("AI-Powered Telecom Solution for Underserved Areas")
    st.write("This app is designed to leverage AI-powered models for optimizing telecommunication infrastructure in underserved areas.")

    st.sidebar.title("Options")
    option = st.sidebar.selectbox(
        "Select a Functionality",
        ("Network Planning Assistance", "Community Content Translation", "Operational Diagnostics", "Workflow Optimization")
    )

    if option == "Network Planning Assistance":
        st.header("Network Planning Assistance")
        address = st.text_input("Enter a location to analyze network suitability:")
        if st.button("Analyze Location"):
            if address:
                geolocator = Nominatim(user_agent="geoapiExercises")
                location = geolocator.geocode(address)
                if location:
                    st.write(f"Location found: {location.address}")
                    st.map(pd.DataFrame(np.array([[location.latitude, location.longitude]]), columns=['lat', 'lon']))
                    st.write("Using Llama models to suggest optimal network deployment...")
                    small_model = load_llama_small_model()
                    large_model = load_llama_large_model()
                    small_result = small_model(f"Provide initial suggestions for network deployment at {location.address}. Consider rural telecommunication needs.", max_length=100)
                    large_result = large_model(f"Provide detailed suggestions for network deployment at {location.address}, considering infrastructure limitations and long-term scalability.", max_length=200)
                    st.success("Initial Suggestions:")
                    st.write(small_result[0]['generated_text'])
                    st.success("Detailed Analysis:")
                    st.write(large_result[0]['generated_text'])
                else:
                    st.error("Location not found. Please try another address.")
            else:
                st.error("Please enter a location to proceed.")

    elif option == "Community Content Translation":
        st.header("Community Content Translation Assistance")
        text_to_translate = st.text_area("Enter content to translate for local communities:")
        if st.button("Translate Content"):
            if text_to_translate:
                small_model = load_llama_small_model()
                large_model = load_llama_large_model()
                translation_result = small_model(f"Translate the following content into a simple language suitable for rural communities: {text_to_translate}", max_length=100)
                classification_model = load_text_classification_model()
                content_type = classification_model(text_to_translate)
                st.success("Translated Content:")
                st.write(translation_result[0]['generated_text'])
                st.success("Content Type Classification:")
                st.write(content_type)
            else:
                st.error("Please enter content to proceed.")

    elif option == "Operational Diagnostics":
        st.header("Operational Maintenance & Network Troubleshooting")
        uploaded_file = st.file_uploader("Upload network diagnostic image (e.g., hardware photo):")
        if uploaded_file is not None:
            st.image(uploaded_file, caption='Uploaded Diagnostic Image', use_column_width=True)
            with st.spinner("Analyzing the image..."):
                vision_model = load_multimodal_model()
                result = vision_model(uploaded_file)
                st.success("Analysis Result:")
                st.write(result)

    elif option == "Workflow Optimization":
        st.header("Workflow Optimization for Telecom Operations")
        st.write("This feature helps optimize administrative and regulatory workflows using AI models.")
        task_description = st.text_area("Enter a workflow task that needs optimization:")
        if st.button("Optimize Workflow"):
            if task_description:
                large_model = load_llama_large_model()
                result = large_model(f"Optimize the following workflow for better efficiency: {task_description}", max_length=150)
                st.success("Optimized Workflow Suggestion:")
                st.write(result[0]['generated_text'])
            else:
                st.error("Please enter a workflow task to proceed.")

if __name__ == "__main__":
    main()