Update app.py

app.py

@@ -1,108 +1,84 @@
 import streamlit as st
 import pandas as pd
-import numpy as np
-from transformers import pipeline
-from geopy.geocoders import Nominatim
+import matplotlib.pyplot as plt
 
-# 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
+# Title of the app
+st.title("AI-Powered Solutions for the Telecommunication Industry in Underserved Areas")
 
-@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
+# Sidebar for navigation
+st.sidebar.title("Navigation")
+options = st.sidebar.radio("Choose a solution:", [
+    "Network Optimization",
+    "Predictive Maintenance",
+    "Dynamic Spectrum Management",
+    "Energy Efficiency",
+    "Customer Support"
+])
 
-@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
+# Network Optimization
+if options == "Network Optimization":
+    st.header("AI-Driven Network Planning")
+    st.write("AI optimizes the placement of cell towers for maximum coverage in underserved areas.")
+    
+    # Simulate tower placement on a map
+    st.subheader("Simulated Tower Placement")
+    data = {
+        "Latitude": [10.0, 10.5, 11.0, 11.5],
+        "Longitude": [76.0, 76.5, 77.0, 77.5],
+        "Coverage Radius (km)": [5, 7, 6, 8]
+    }
+    df = pd.DataFrame(data)
+    st.map(df)
 
-@st.cache_resource
-def load_text_classification_model():
-    model_name = "distilbert-base-uncased"
-    text_classifier = pipeline("text-classification", model=model_name)
-    return text_classifier
+# Predictive Maintenance
+elif options == "Predictive Maintenance":
+    st.header("Predictive Maintenance with AI")
+    st.write("AI predicts equipment failures to reduce downtime and maintenance costs.")
+    
+    # Simulate failure predictions
+    st.subheader("Equipment Failure Predictions")
+    equipment_data = {
+        "Equipment ID": [1, 2, 3, 4],
+        "Failure Probability (%)": [10, 25, 5, 50]
+    }
+    df = pd.DataFrame(equipment_data)
+    st.bar_chart(df.set_index("Equipment ID"))
 
-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.")
+# Dynamic Spectrum Management
+elif options == "Dynamic Spectrum Management":
+    st.header("AI for Dynamic Spectrum Allocation")
+    st.write("AI dynamically allocates spectrum resources to reduce congestion and improve efficiency.")
+    
+    # Simulate spectrum allocation
+    st.subheader("Spectrum Usage Over Time")
+    spectrum_data = {
+        "Time (hours)": [1, 2, 3, 4, 5],
+        "Spectrum Utilization (%)": [30, 50, 70, 60, 40]
+    }
+    df = pd.DataFrame(spectrum_data)
+    st.line_chart(df.set_index("Time (hours)"))
 
-    st.sidebar.title("Options")
-    option = st.sidebar.selectbox(
-        "Select a Functionality",
-        ("Network Planning Assistance", "Community Content Translation", "Operational Diagnostics", "Workflow Optimization")
-    )
+# Energy Efficiency
+elif options == "Energy Efficiency":
+    st.header("AI for Energy Optimization")
+    st.write("AI optimizes energy consumption in telecom infrastructure using renewable energy sources.")
+    
+    # Simulate energy usage
+    st.subheader("Energy Consumption Over Time")
+    energy_data = {
+        "Time (hours)": [1, 2, 3, 4, 5],
+        "Energy Consumption (kWh)": [100, 80, 90, 70, 60]
+    }
+    df = pd.DataFrame(energy_data)
+    st.area_chart(df.set_index("Time (hours)"))
 
-    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()
+# Customer Support
+elif options == "Customer Support":
+    st.header("AI-Powered Customer Support")
+    st.write("AI chatbots provide 24/7 support for users in underserved areas.")
+    
+    # Simulate a chatbot
+    st.subheader("Chatbot Demo")
+    user_input = st.text_input("Ask a question:")
+    if user_input:
+        st.write(f"AI Response: Thank you for your question! We will assist you shortly.")