Create app.py

app.py

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+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()