Create app.py

app.py

@@ -0,0 +1,93 @@
+import os
+import pandas as pd
+import streamlit as st
+import matplotlib.pyplot as plt
+import seaborn as sns
+from groq import Groq
+from typing import List
+
+# ---- Setup Groq Client ---- #
+client = Groq(api_key=("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"))
+
+# ---- File Upload ---- #
+st.title("🚦 Real-Time Traffic Optimization using RAG + Groq")
+st.subheader("Upload Traffic Data (CSV)")
+
+# Upload CSV file
+uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+if uploaded_file is not None:
+    # Read the CSV file into a DataFrame
+    df = pd.read_csv(uploaded_file)
+    
+    # ---- Utility: Summarize traffic data to context ---- #
+    def summarize_traffic_data(df: pd.DataFrame) -> List[str]:
+        summaries = []
+        for index, row in df.iterrows():
+            summary = (
+                f"On {row['Date']} ({row['Day of the week']}) at {row['Time']}, "
+                f"there were {row['CarCount']} cars, {row['BikeCount']} bikes, "
+                f"{row['BusCount']} buses, and {row['TruckCount']} trucks. "
+                f"Total traffic: {row['Total']}. Situation: {row['Traffic Situation']}."
+            )
+            summaries.append(summary)
+        return summaries
+
+    # ---- Prompt Constructor ---- #
+    def generate_traffic_prompt(user_query: str, context: List[str]) -> str:
+        context_text = "\n".join(context)
+        prompt = f"""
+    Context:
+    {context_text}
+
+    User Query:
+    {user_query}
+
+    Based on the context above, generate a traffic optimization strategy.
+    """
+        return prompt
+
+    # ---- Groq Query ---- #
+    def get_optimization_recommendation(prompt: str) -> str:
+        response = client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model="llama-3-70b-versatile",
+            stream=False
+        )
+        return response.choices[0].message.content
+
+    # ---- Data Visualization ---- #
+    st.subheader("📊 Traffic Data Visualization")
+    st.dataframe(df.head(10))
+
+    st.write("### Traffic Volume Over Time")
+    fig, ax = plt.subplots(figsize=(10, 4))
+    df['Timestamp'] = pd.to_datetime(df['Date'] + ' ' + df['Time'])
+    df_sorted = df.sort_values("Timestamp")
+    ax.plot(df_sorted['Timestamp'], df_sorted['Total'], marker='o')
+    ax.set_xlabel("Time")
+    ax.set_ylabel("Total Traffic Volume")
+    ax.set_title("Traffic Volume Over Time")
+    st.pyplot(fig)
+
+    st.write("### Vehicle Count Distribution")
+    fig2, ax2 = plt.subplots(figsize=(10, 4))
+    df[['CarCount', 'BikeCount', 'BusCount', 'TruckCount']].plot(kind='box', ax=ax2)
+    ax2.set_title("Distribution of Vehicle Counts")
+    st.pyplot(fig2)
+
+    # ---- User Query and RAG Output ---- #
+    user_query = st.text_area("Enter your traffic-related query")
+    if user_query:
+        with st.spinner("Processing traffic data and generating strategy..."):
+            traffic_context = summarize_traffic_data(df)
+            prompt = generate_traffic_prompt(user_query, traffic_context[:10])  # Limit to first 10 rows
+            result = get_optimization_recommendation(prompt)
+            st.success("Strategy Generated:")
+            st.write(result)
+
+else:
+    st.warning("Please upload a CSV file to proceed.")
+
+st.markdown("---")
+st.caption("🔁 This app analyzes traffic data using RAG + Groq and visualizes traffic patterns.")