Create app.py

app.py

@@ -0,0 +1,93 @@
+# app.py
+import streamlit as st
+import pandas as pd
+from llama_index import GPTVectorStoreIndex, Document
+from sklearn.metrics import accuracy_score, precision_score, recall_score
+import json
+import autogen
+from pathlib import Path
+from modules.data_ingestion import DataIngestionModule
+from modules.prompt_engineering import PromptEngineeringModule
+from modules.evaluation import EvaluationModule
+
+class LlamaDeveloperAid:
+    def __init__(self):
+        st.set_page_config(page_title="Llama Developer Aid", layout="wide")
+        self.data_module = DataIngestionModule()
+        self.prompt_module = PromptEngineeringModule()
+        self.eval_module = EvaluationModule()
+
+    def main(self):
+        st.title("Llama Developer Aid")
+        
+        # Sidebar for navigation
+        page = st.sidebar.selectbox(
+            "Select Module",
+            ["Data Ingestion", "Prompt Engineering", "Evaluation"]
+        )
+        
+        if page == "Data Ingestion":
+            self.render_data_ingestion()
+        elif page == "Prompt Engineering":
+            self.render_prompt_engineering()
+        else:
+            self.render_evaluation()
+
+    def render_data_ingestion(self):
+        st.header("Data Ingestion Module")
+        
+        # File upload
+        uploaded_file = st.file_uploader("Upload your dataset", type=["csv", "xlsx", "json"])
+        
+        if uploaded_file:
+            df = self.data_module.load_data(uploaded_file)
+            st.dataframe(df)
+            
+            if st.button("Process and Index Data"):
+                with st.spinner("Processing..."):
+                    indexed_data = self.data_module.index_data(df)
+                    st.success("Data processed and indexed successfully!")
+                    st.session_state['indexed_data'] = indexed_data
+
+    def render_prompt_engineering(self):
+        st.header("Prompt Engineering Module")
+        
+        # Load templates
+        templates = self.prompt_module.load_templates()
+        selected_template = st.selectbox("Select Prompt Template", list(templates.keys()))
+        
+        # Display template details
+        if selected_template:
+            template = templates[selected_template]
+            prompt_text = st.text_area("Customize Prompt", template['prompt'])
+            
+            if st.button("Generate Response"):
+                with st.spinner("Generating..."):
+                    response = self.prompt_module.generate_response(prompt_text)
+                    st.write("Generated Response:", response)
+
+    def render_evaluation(self):
+        st.header("Evaluation Module")
+        
+        if 'indexed_data' not in st.session_state:
+            st.warning("Please process data in the Data Ingestion module first")
+            return
+        
+        # Evaluation metrics
+        metrics = st.multiselect(
+            "Select Evaluation Metrics",
+            ["Accuracy", "Precision", "Recall"]
+        )
+        
+        if st.button("Run Evaluation"):
+            with st.spinner("Evaluating..."):
+                results = self.eval_module.evaluate_model(
+                    st.session_state['indexed_data'],
+                    metrics
+                )
+                st.write("Evaluation Results:", results)
+
+if __name__ == "__main__":
+    app = LlamaDeveloperAid()
+    app.main()
+Last edited just now