Improve LLM use case and fix dashboard bugs.

.env.example

@@ -1,4 +1,4 @@
-openai_api_key=
+OPENAI_API_KEY=
 LANGCHAIN_TRACING_V2=true
 LANGCHAIN_ENDPOINT=https://api.smith.langchain.com
 LANGCHAIN_PROJECT=autocode

README.md

@@ -25,15 +25,15 @@ companies.
 1. Install the requirements
 
 ```bash
-pip install autocode-py
+pip install -U autocode-py
 ```
 
 2. Prepare software to be processed as in the [`./example/client`](https://github.com/muazhari/autocode/tree/main/example/client) folder.
 3. Prepare deployment as in the [`./example/client/docker-compose.yml`](https://github.com/muazhari/autocode/blob/main/example/client/docker-compose.yml) file.
 4. Prepare controller as in the [`./example/controller.ipynb`](https://github.com/muazhari/autocode/blob/main/example/controller.ipynb) file.
 5. Instantiate `optimization` and execute `optimization.deploy()` in controller.
 6. Open dashboard in `http://localhost:{dashboard_port}/` to see the process in real-time.
-7. Wait until all client are ready (need to wait for long time because the libraries need to be re-download for each client & refresh the dashboard manually because of bug).
+7. Wait until all client are ready (need to wait for long time because the libraries need to be re-downloaded for each client).
 8. Execute `optimization.run()` in controller.
 9. Wait until the run is finished.
 10. Analyze and decide the best values.

autocode/dashboard.py

@@ -55,16 +55,19 @@
         client: OptimizationClient = dill.loads(client_cache.value)
         variables.update(client.variables)
         clients[client.id] = client
-        client_df_list.append(client.model_dump(mode="json"))
+        client_json: Dict[str, Any] = client.model_dump(mode="json")
+        client_json["port"] = str(client.port)
+        client_df_list.append(client_json)
 
-    with client_df_placeholder:
-        client_df: pd.DataFrame = pd.DataFrame(client_df_list)
-        client_df = client_df.astype(dtype=str)
+    client_df: pd.DataFrame = pd.DataFrame(client_df_list)
+
+    with client_df_placeholder.container():
         st.dataframe(client_df, height=500)
 
     if len(objective_caches) > 0:
         break
 
+    session.close()
     time.sleep(0.01)
 
 if len(objective_caches) == 0 and len(client_caches) == 0:
@@ -91,12 +94,7 @@
 else:
     st.error("Preparation data cache is not valid.")
 
-plot_0_placeholder = st.empty()
-plot_1_placeholder = st.empty()
-st.subheader("Objective Space")
-plot_f_df_placeholder = st.empty()
-st.subheader("Solution Space")
-plot_x_df_placeholder = st.empty()
+plot_placeholder = st.empty()
 
 while True:
     try:
@@ -137,12 +135,6 @@
             decision_index=decision_index
         )
 
-        with plot_0_placeholder:
-            st.pyplot(plots[0].fig)
-
-        with plot_1_placeholder:
-            st.pyplot(plots[1].fig)
-
         list_dict_x: List[List[Dict[str, Any]]] = []
         list_dict_f: List[Dict[str, Any]] = []
         for index, (x, f) in enumerate(zip(result.X, result.F)):
@@ -178,13 +170,17 @@
                 dict_f[f"f{index + 1}"] = f_value
             list_dict_f.append(dict_f)
 
-        with plot_f_df_placeholder:
-            f_df: pd.DataFrame = pd.DataFrame(list_dict_f)
-            st.dataframe(f_df, height=500)
+        f_df: pd.DataFrame = pd.DataFrame(list_dict_f)
 
-        with plot_x_df_placeholder:
+        with plot_placeholder.container():
+            st.subheader("Objective Space")
+            st.pyplot(plots[0].fig)
+            st.pyplot(plots[1].fig)
+            st.subheader("Solution Space")
+            st.dataframe(f_df, height=500)
             st.json(list_dict_x, expanded=False)
 
         st.session_state["old_result_caches"].add(cache)
 
+    session.close()
     time.sleep(0.01)

autocode/model.py

@@ -22,42 +22,35 @@ class BaseModel(PydanticBaseModelV2):
 
 class CodeScoring(BaseModelV1):
     """
-    Score code in based on the following statements:
-    Error Potentiality - this code is potentially error-prone;
-    Readability - this code is easy to read;
-    Understandability - the semantic meaning of this code is clear;
-    Complexity - this code is complex;
-    Modularity  - this code should be broken into smaller pieces;
-    Overall maintainability - overall, this code is maintainable.
-    The score scale from 1 (strongly agree) to 100 (strongly disagree).
-    You must score in precision, i.e. 14.3, 47.456, 75.45, 58.58495, 3.141598, etc.
+    Score the code.
     """
-    error_potentiality: float = FieldV1(description="Error potential score.")
+    analysis: List[str] = FieldV1(description="Step-by-step analysis before scoring the code.")
     readability: float = FieldV1(description="Readability score.")
     understandability: float = FieldV1(description="Understandability score.")
     complexity: float = FieldV1(description="Complexity score.")
     modularity: float = FieldV1(description="Modularity score.")
+    error_potentiality: float = FieldV1(description="Error potential score.")
     overall_maintainability: float = FieldV1(description="Overall maintainability score.")
 
 
 class CodeVariation(BaseModelV1):
     """
-    Code variation is a code snippet that is a variation of the original code.
+    Propose code variation.
     """
-    variation: Optional[str] = FieldV1(description="Code variation.", default=None)
+    analysis: List[str] = FieldV1(description="Step-by-step analysis before proposing the code variation.")
+    variation: str = FieldV1(description="Proposed code variation.")
 
 
 class ScoringState(TypedDict):
-    code: str
-    analysis: str
-    score: List[CodeScoring]
+    programming_language: str
+    existing_code: str
+    score: CodeScoring
 
 
 class VariationState(TypedDict):
-    code: str
-    analysis: str
-    variation: List[CodeVariation]
-    new_function_name: str
+    programming_language: str
+    existing_code: str
+    variations: List[CodeVariation]
 
 
 class OptimizationVariable(BaseModel):
@@ -141,6 +134,7 @@ class OptimizationClient(BaseModel):
 
 
 class OptimizationPrepareRequest(BaseModel):
+    language: str
     variables: Dict[str, OptimizationBinary | OptimizationChoice | OptimizationInteger | OptimizationReal]
     host: Optional[str] = Field(default=None)
     port: int

autocode/setting.py

@@ -12,4 +12,4 @@ class ApplicationSetting(BaseSettings):
     server_host: str = Field(default="0.0.0.0")
     server_port: int = Field(default=10000)
     dashboard_port: int = Field(default=10001)
-    openai_api_key: str = Field(default="")
+    OPENAI_API_KEY: str = Field(default="")