Create interface.py

interface.py

@@ -0,0 +1,178 @@
+# interface.py
+
+import streamlit as st
+import logging
+from typing import Dict
+
+from langchain_core.messages import HumanMessage
+from workflow import ResearchWorkflow
+from config import ResearchConfig
+from langchain_core.messages import AIMessage
+
+logger = logging.getLogger(__name__)
+
+class ResearchInterface:
+    """
+    Provides the Streamlit-based interface for executing the research workflow.
+    """
+    def __init__(self) -> None:
+        self.workflow = ResearchWorkflow()
+        self._initialize_interface()
+
+    def _initialize_interface(self) -> None:
+        st.set_page_config(
+            page_title="NeuroResearch AI",
+            layout="wide",
+            initial_sidebar_state="expanded"
+        )
+        self._inject_styles()
+        self._build_sidebar()
+        self._build_main_interface()
+
+    def _inject_styles(self) -> None:
+        st.markdown(
+            """
+            <style>
+            :root {
+                --primary: #2ecc71;
+                --secondary: #3498db;
+                --background: #0a0a0a;
+                --text: #ecf0f1;
+            }
+            .stApp {
+                background: var(--background);
+                color: var(--text);
+                font-family: 'Roboto', sans-serif;
+            }
+            .stTextArea textarea {
+                background: #1a1a1a !important;
+                color: var(--text) !important;
+                border: 2px solid var(--secondary);
+                border-radius: 8px;
+                padding: 1rem;
+            }
+            .stButton>button {
+                background: linear-gradient(135deg, var(--primary), var(--secondary));
+                border: none;
+                border-radius: 8px;
+                padding: 1rem 2rem;
+                transition: all 0.3s;
+            }
+            .stButton>button:hover {
+                transform: translateY(-2px);
+                box-shadow: 0 4px 12px rgba(46, 204, 113, 0.3);
+            }
+            .stExpander {
+                background: #1a1a1a;
+                border: 1px solid #2a2a2a;
+                border-radius: 8px;
+                margin: 1rem 0;
+            }
+            </style>
+            """,
+            unsafe_allow_html=True
+        )
+
+    def _build_sidebar(self) -> None:
+        with st.sidebar:
+            st.title("🔍 Research Database")
+            st.subheader("Technical Papers")
+            for title, short in ResearchConfig.DOCUMENT_MAP.items():
+                with st.expander(short):
+                    st.markdown(f"```\n{title}\n```")
+            st.subheader("Analysis Metrics")
+            st.metric("Vector Collections", 2)
+            st.metric("Embedding Dimensions", ResearchConfig.EMBEDDING_DIMENSIONS)
+            with st.sidebar.expander("Collaboration Hub"):
+                st.subheader("Live Research Team")
+                st.write("👩💻 Researcher A")
+                st.write("👨🔬 Researcher B")
+                st.write("🤖 AI Assistant")
+                st.subheader("Knowledge Graph")
+                if st.button("🕸 View Current Graph"):
+                    self._display_knowledge_graph()
+
+    def _build_main_interface(self) -> None:
+        st.title("🧠 NeuroResearch AI")
+        query = st.text_area("Research Query:", height=200, placeholder="Enter technical research question...")
+        domain = st.selectbox(
+            "Select Research Domain:",
+            options=[
+                "Biomedical Research",
+                "Legal Research",
+                "Environmental and Energy Studies",
+                "Competitive Programming and Theoretical Computer Science",
+                "Social Sciences"
+            ],
+            index=0
+        )
+        if st.button("Execute Analysis", type="primary"):
+            self._execute_analysis(query, domain)
+
+    def _execute_analysis(self, query: str, domain: str) -> None:
+        try:
+            with st.spinner("Initializing Quantum Analysis..."):
+                results = self.workflow.app.stream(
+                    {
+                        "messages": [HumanMessage(content=query)],
+                        "context": {"domain": domain},
+                        "metadata": {}
+                    },
+                    {"recursion_limit": 100}
+                )
+                for event in results:
+                    self._render_event(event)
+                st.success("✅ Analysis Completed Successfully")
+        except Exception as e:
+            st.error(
+                f"""**Analysis Failed**  
+{str(e)}  
+Potential issues:
+- Complex query structure
+- Document correlation failure
+- Temporal processing constraints"""
+            )
+
+    def _render_event(self, event: Dict) -> None:
+        if 'ingest' in event:
+            with st.container():
+                st.success("✅ Query Ingested")
+        elif 'retrieve' in event:
+            with st.container():
+                docs = event['retrieve']['context'].get('documents', [])
+                st.info(f"📚 Retrieved {len(docs)} documents")
+                with st.expander("View Retrieved Documents", expanded=False):
+                    for idx, doc in enumerate(docs, start=1):
+                        st.markdown(f"**Document {idx}**")
+                        st.code(doc.page_content, language='text')
+        elif 'analyze' in event:
+            with st.container():
+                content = event['analyze']['messages'][0].content
+                with st.expander("Technical Analysis Report", expanded=True):
+                    st.markdown(content)
+        elif 'validate' in event:
+            with st.container():
+                content = event['validate']['messages'][0].content
+                if "VALID" in content:
+                    st.success("✅ Validation Passed")
+                    with st.expander("View Validated Analysis", expanded=True):
+                        st.markdown(content.split("Validation:")[0])
+                else:
+                    st.warning("⚠️ Validation Issues Detected")
+                    with st.expander("View Validation Details", expanded=True):
+                        st.markdown(content)
+        elif 'enhance' in event:
+            with st.container():
+                content = event['enhance']['messages'][0].content
+                with st.expander("Enhanced Multi-Modal Analysis Report", expanded=True):
+                    st.markdown(content)
+
+    def _display_knowledge_graph(self) -> None:
+        st.write("Knowledge Graph visualization is not implemented yet.")
+
+class ResearchInterfaceExtended(ResearchInterface):
+    """
+    Extended interface that includes domain adaptability, collaboration features, and graph visualization.
+    """
+    def _build_main_interface(self) -> None:
+        super()._build_main_interface()