Refactor llm vii

alphastats/DataSet.py

@@ -311,6 +311,27 @@ def plot_umap(self, group: Optional[str] = None, circle: bool = False):
         )
         return dimensionality_reduction.plot
 
+    def perform_dimensionality_reduction(
+        self, method: str, group: Optional[str] = None, circle: bool = False
+    ):
+        """Generic wrapper for dimensionality reduction methods to be used by LLM.
+
+        Args:
+            method (str): "pca", "tsne", "umap"
+            group (str, optional): column in metadata that should be used for coloring. Defaults to None.
+            circle (bool, optional): draw circle around each group. Defaults to False.
+        """
+
+        result = {
+            "pca": self.plot_pca,
+            "tsne": self.plot_tsne,
+            "umap": self.plot_umap,
+        }.get(method)
+        if result is None:
+            raise ValueError(f"Invalid method: {method}")
+
+        return result(group=group, circle=circle)
+
     @ignore_warning(RuntimeWarning)
     def plot_volcano(
         self,

alphastats/gui/pages/04_Analysis.py

@@ -89,7 +89,7 @@ def show_start_llm_button(method: str) -> None:
 
     msg = (
         "(this will overwrite the existing LLM analysis!)"
-        if StateKeys.LLM_INTEGRATION in st.session_state
+        if st.session_state.get(StateKeys.LLM_INTEGRATION, {}) != {}
         else ""
     )
 

alphastats/gui/pages/05_LLM.py

@@ -8,9 +8,9 @@
     display_figure,
 )
 from alphastats.gui.utils.llm_helper import (
-    display_proteins,
+    get_display_proteins_html,
+    llm_connection_test,
     set_api_key,
-    test_llm_connection,
 )
 from alphastats.gui.utils.ui_helper import StateKeys, init_session_state, sidebar_info
 from alphastats.llm.llm_integration import LLMIntegration, Models
@@ -36,20 +36,24 @@ def llm_config():
     """Show the configuration options for the LLM analysis."""
     c1, _ = st.columns((1, 2))
     with c1:
-        model_before = st.session_state.get(StateKeys.API_TYPE, None)
+        current_model = st.session_state.get(StateKeys.MODEL_NAME, None)
 
-        st.session_state[StateKeys.API_TYPE] = st.selectbox(
+        models = [Models.GPT4O, Models.OLLAMA_31_70B, Models.OLLAMA_31_8B]
+        st.session_state[StateKeys.MODEL_NAME] = st.selectbox(
             "Select LLM",
-            [Models.GPT4O, Models.OLLAMA_31_70B, Models.OLLAMA_31_8B],
+            models,
+            index=models.index(st.session_state.get(StateKeys.MODEL_NAME))
+            if current_model is not None
+            else 0,
         )
 
         base_url = None
-        if st.session_state[StateKeys.API_TYPE] in [Models.GPT4O]:
+        if st.session_state[StateKeys.MODEL_NAME] in [Models.GPT4O]:
             api_key = st.text_input(
                 "Enter OpenAI API Key and press Enter", type="password"
             )
             set_api_key(api_key)
-        elif st.session_state[StateKeys.API_TYPE] in [
+        elif st.session_state[StateKeys.MODEL_NAME] in [
             Models.OLLAMA_31_70B,
             Models.OLLAMA_31_8B,
         ]:
@@ -58,13 +62,18 @@ def llm_config():
 
         test_connection = st.button("Test connection")
         if test_connection:
-            test_llm_connection(
-                api_type=st.session_state[StateKeys.API_TYPE],
-                api_key=st.session_state[StateKeys.OPENAI_API_KEY],
-                base_url=base_url,
-            )
-
-        if model_before != st.session_state[StateKeys.API_TYPE]:
+            with st.spinner(f"Testing connection to {model_name}.."):
+                error = llm_connection_test(
+                    model_name=st.session_state[StateKeys.MODEL_NAME],
+                    api_key=st.session_state[StateKeys.OPENAI_API_KEY],
+                    base_url=base_url,
+                )
+                if error is None:
+                    st.success(f"Connection to {model_name} successful!")
+                else:
+                    st.error(f"Connection to {model_name} failed: {str(error)}")
+
+        if current_model != st.session_state[StateKeys.MODEL_NAME]:
             st.rerun(scope="app")
 
 
@@ -120,17 +129,24 @@ def llm_config():
     c1, c2 = st.columns((1, 2), gap="medium")
     with c1:
         st.write("Upregulated genes")
-        display_proteins(upregulated_genes, [])
+        st.markdown(
+            get_display_proteins_html(upregulated_genes, True), unsafe_allow_html=True
+        )
+
     with c2:
         st.write("Downregulated genes")
-        display_proteins([], downregulated_genes)
+        st.markdown(
+            get_display_proteins_html(downregulated_genes, False),
+            unsafe_allow_html=True,
+        )
 
 
 st.markdown("##### Prompts generated based on analysis input")
 
-api_type = st.session_state[StateKeys.API_TYPE]
+model_name = st.session_state[StateKeys.MODEL_NAME]
 llm_integration_set_for_model = (
-    st.session_state.get(StateKeys.LLM_INTEGRATION, {}).get(api_type, None) is not None
+    st.session_state.get(StateKeys.LLM_INTEGRATION, {}).get(model_name, None)
+    is not None
 )
 with st.expander("System message", expanded=False):
     system_message = st.text_area(
@@ -151,30 +167,39 @@ def llm_config():
     )
 
 
-st.markdown(f"##### LLM Analysis with {api_type}")
+st.markdown(f"##### LLM Analysis with {model_name}")
 
-llm_submitted = st.button(
+c1, c2, _ = st.columns((0.2, 0.2, 0.6))
+llm_submitted = c1.button(
     "Run LLM analysis ...", disabled=llm_integration_set_for_model
 )
 
-if st.session_state[StateKeys.LLM_INTEGRATION].get(api_type) is None:
+llm_reset = c2.button(
+    "Reset LLM analysis ...", disabled=not llm_integration_set_for_model
+)
+if llm_reset:
+    del st.session_state[StateKeys.LLM_INTEGRATION]
+    st.rerun()
+
+
+if st.session_state[StateKeys.LLM_INTEGRATION].get(model_name) is None:
     if not llm_submitted:
         st.stop()
 
     try:
         llm_integration = LLMIntegration(
-            api_type=api_type,
+            model_name=model_name,
             system_message=system_message,
             api_key=st.session_state[StateKeys.OPENAI_API_KEY],
             base_url=OLLAMA_BASE_URL,
             dataset=st.session_state[StateKeys.DATASET],
             gene_to_prot_id_map=gene_to_prot_id_map,
         )
 
-        st.session_state[StateKeys.LLM_INTEGRATION][api_type] = llm_integration
+        st.session_state[StateKeys.LLM_INTEGRATION][model_name] = llm_integration
 
         st.success(
-            f"{st.session_state[StateKeys.API_TYPE]} integration initialized successfully!"
+            f"{st.session_state[StateKeys.MODEL_NAME]} integration initialized successfully!"
         )
 
         with st.spinner("Processing initial prompt..."):
@@ -221,4 +246,4 @@ def llm_chat(llm_integration: LLMIntegration, show_all: bool = False):
     help="Show all messages in the chat interface.",
 )
 
-llm_chat(st.session_state[StateKeys.LLM_INTEGRATION][api_type], show_all)
+llm_chat(st.session_state[StateKeys.LLM_INTEGRATION][model_name], show_all)

alphastats/gui/utils/llm_helper.py

@@ -7,32 +7,24 @@
 from alphastats.llm.llm_integration import LLMIntegration
 
 
-def display_proteins(overexpressed: List[str], underexpressed: List[str]) -> None:
+def get_display_proteins_html(protein_ids: List[str], is_upregulated: True) -> str:
     """
-    Display a list of overexpressed and underexpressed proteins in a Streamlit app.
+    Get HTML code for displaying a list of proteins, color according to expression.
 
     Args:
-        overexpressed (list[str]): A list of overexpressed proteins.
-        underexpressed (list[str]): A list of underexpressed proteins.
+        protein_ids (list[str]): a list of proteins.
+        is_upregulated (bool): whether the proteins are up- or down-regulated.
     """
 
-    # Start with the overexpressed proteins
-    link = "https://www.uniprot.org/uniprotkb?query="
-    overexpressed_html = "".join(
-        f'<a href = {link + protein}><li style="color: green;">{protein}</li></a>'
-        for protein in overexpressed
-    )
-    # Continue with the underexpressed proteins
-    underexpressed_html = "".join(
-        f'<a href = {link + protein}><li style="color: red;">{protein}</li></a>'
-        for protein in underexpressed
-    )
+    uniprot_url = "https://www.uniprot.org/uniprotkb?query="
 
-    # Combine both lists into one HTML string
-    full_html = f"<ul>{overexpressed_html}{underexpressed_html}</ul>"
+    color = "green" if is_upregulated else "red"
+    protein_ids_html = "".join(
+        f'<a href = {uniprot_url + protein}><li style="color: {color};">{protein}</li></a>'
+        for protein in protein_ids
+    )
 
-    # Display in Streamlit
-    st.markdown(full_html, unsafe_allow_html=True)
+    return f"<ul>{protein_ids_html}</ul>"
 
 
 def set_api_key(api_key: str = None) -> None:
@@ -71,21 +63,20 @@ def set_api_key(api_key: str = None) -> None:
     st.session_state[StateKeys.OPENAI_API_KEY] = api_key
 
 
-def test_llm_connection(
-    api_type: str,
+def llm_connection_test(
+    model_name: str,
     base_url: Optional[str] = None,
     api_key: Optional[str] = None,
-):
-    """Test the connection to the LLM API."""
+) -> Optional[str]:
+    """Test the connection to the LLM API, return None in case of success, error message otherwise."""
     try:
-        llm = LLMIntegration(api_type, base_url=base_url, api_key=api_key)
-
-        with st.spinner(f"Testing connection to {api_type} ..."):
-            llm.chat_completion("Hello, this is a test!")
-
-        st.success(f"Connection to {api_type} successful!")
-        return True
+        llm = LLMIntegration(
+            model_name, base_url=base_url, api_key=api_key, load_tools=False
+        )
+        llm.chat_completion(
+            "This is a test. Simply respond 'yes' if you got this message."
+        )
+        return None
 
     except Exception as e:
-        st.error(f"❌ Connection to {api_type} failed: {e}")
-        return False
+        return str(e)

alphastats/gui/utils/ui_helper.py

@@ -108,7 +108,7 @@ class StateKeys:
 
     # LLM
     OPENAI_API_KEY = "openai_api_key"  # pragma: allowlist secret
-    API_TYPE = "api_type"
+    MODEL_NAME = "model_name"
 
     LLM_INPUT = "llm_input"
 

alphastats/llm/llm_functions.py

@@ -4,7 +4,14 @@
 
 import pandas as pd
 
-from alphastats.plots.DimensionalityReduction import DimensionalityReduction
+from alphastats.DataSet import DataSet
+from alphastats.llm.enrichment_analysis import get_enrichment_data
+from alphastats.llm.uniprot_utils import get_gene_function
+
+GENERAL_FUNCTION_MAPPING = {
+    "get_gene_function": get_gene_function,
+    "get_enrichment_data": get_enrichment_data,
+}
 
 
 def get_general_assistant_functions() -> List[Dict]:
@@ -17,7 +24,7 @@ def get_general_assistant_functions() -> List[Dict]:
         {
             "type": "function",
             "function": {
-                "name": "get_gene_function",
+                "name": get_gene_function.__name__,
                 "description": "Get the gene function and description by UniProt lookup of gene identifier/name",
                 "parameters": {
                     "type": "object",
@@ -34,7 +41,7 @@ def get_general_assistant_functions() -> List[Dict]:
         {
             "type": "function",
             "function": {
-                "name": "get_enrichment_data",
+                "name": get_enrichment_data.__name__,
                 "description": "Get enrichment data for a list of differentially expressed genes",
                 "parameters": {
                     "type": "object",
@@ -83,12 +90,12 @@ def get_assistant_functions(
         {
             "type": "function",
             "function": {
-                "name": "plot_intensity",
+                "name": DataSet.plot_intensity.__name__,
                 "description": "Create an intensity plot based on protein data and analytical methods.",
                 "parameters": {
                     "type": "object",
                     "properties": {
-                        "gene_name": {  # this will be mapped to "protein_id" when calling the function
+                        "protein_id": {  # LLM will provide gene_name, mapping to protein_id is done when calling the function
                             "type": "string",
                             "enum": gene_names,
                             "description": "Identifier for the gene of interest",
@@ -125,7 +132,7 @@ def get_assistant_functions(
         {
             "type": "function",
             "function": {
-                "name": "perform_dimensionality_reduction",
+                "name": DataSet.perform_dimensionality_reduction.__name__,
                 "description": "Perform dimensionality reduction on a given dataset and generate a plot.",
                 "parameters": {
                     "type": "object",
@@ -152,7 +159,7 @@ def get_assistant_functions(
         {
             "type": "function",
             "function": {
-                "name": "plot_sampledistribution",
+                "name": DataSet.plot_sampledistribution.__name__,
                 "description": "Generates a histogram plot for each sample in the dataset matrix.",
                 "parameters": {
                     "type": "object",
@@ -175,7 +182,7 @@ def get_assistant_functions(
         {
             "type": "function",
             "function": {
-                "name": "plot_volcano",
+                "name": DataSet.plot_volcano.__name__,
                 "description": "Generates a volcano plot based on two subgroups of the same group",
                 "parameters": {
                     "type": "object",
@@ -235,17 +242,3 @@ def get_assistant_functions(
         },
         # {"type": "code_interpreter"},
     ]
-
-
-def perform_dimensionality_reduction(dataset, group, method, circle, **kwargs):
-    dr = DimensionalityReduction(
-        mat=dataset.mat,
-        metadate=dataset.metadata,
-        sample=dataset.sample,
-        preprocessing_info=dataset.preprocessing_info,
-        group=group,
-        circle=circle,
-        method=method,
-        **kwargs,
-    )
-    return dr.plot