Add Gradio UI and Dockerfile for HF Space deployment

Dockerfile

@@ -0,0 +1,33 @@
+# Use a standard Python image
+FROM python:3.12-slim
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    git \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install uv
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
+    mv /root/.local/bin/uv /usr/local/bin/uv
+
+# Set working directory
+WORKDIR /app
+
+# Copy project files
+COPY . .
+
+# Install dependencies using uv
+# We use --frozen if uv.lock is present, but for HF Space we might want to be flexible
+RUN uv sync --no-cache
+
+# Set environment variables
+ENV PATH="/app/.venv/bin:$PATH"
+ENV PYTHONPATH="/app:$PYTHONPATH"
+ENV PYTHONUNBUFFERED=1
+
+# Expose the port Gradio will run on
+EXPOSE 8000
+
+# Default command to run the Gradio app
+CMD ["python", "app.py"]

app.py

@@ -0,0 +1,232 @@
+import os
+import sys
+import asyncio
+import json
+import gradio as gr
+from typing import Optional, Dict, Any, List
+from dotenv import load_dotenv
+from openai import AsyncOpenAI
+
+# Ensure the project root is in sys.path
+sys.path.append(os.path.abspath(os.path.dirname(__file__)))
+
+try:
+    from earnings_analyst.server.earnings_analyst_environment import EarningsAnalystEnvironment
+    from earnings_analyst.models import EarningsAnalystAction, EarningsAnalystObservation
+    from earnings_analyst.tasks.registry import TASK_IDS, TASKS
+except (ImportError, ModuleNotFoundError):
+    from server.earnings_analyst_environment import EarningsAnalystEnvironment
+    from models import EarningsAnalystAction, EarningsAnalystObservation
+    from tasks.registry import TASK_IDS, TASKS
+
+load_dotenv()
+
+class State:
+    def __init__(self):
+        self.env: Optional[EarningsAnalystEnvironment] = None
+        self.obs: Optional[EarningsAnalystObservation] = None
+        self.task_id: str = "sentiment_label"
+
+state = State()
+
+async def reset_env(task_id: str):
+    state.task_id = task_id
+    state.env = EarningsAnalystEnvironment(task_id=task_id)
+    state.obs = state.env.reset()
+    
+    # Format observation for display
+    text_context = ""
+    if state.obs.text_context:
+        for name, text in sorted(state.obs.text_context.items()):
+            text_context += f"### {name}\n{text}\n\n"
+            
+    numerical_context = json.dumps(state.obs.numerical_context, indent=2) if state.obs.numerical_context else "No numerical data."
+    
+    return [
+        state.obs.task_instruction,
+        text_context,
+        numerical_context,
+        gr.update(visible=True), # Prediction row
+        gr.update(visible=False), # Result row
+        "", # Prediction input
+        "", # Log/Message
+    ]
+
+async def step_env(prediction: str):
+    if not state.env or not state.obs:
+        return [gr.update(), "Error: Environment not initialized. Click Reset."]
+    
+    action = EarningsAnalystAction(prediction=prediction)
+    state.obs = state.env.step(action)
+    
+    reward = state.obs.reward
+    ground_truth = getattr(state.obs, "ground_truth", "N/A")
+    
+    result_text = f"**Reward:** {reward:.4f} \n\n**Ground Truth:** {ground_truth}"
+    
+    return [
+        gr.update(visible=False), # Hide prediction row
+        gr.update(visible=True, value=result_text), # Show result row
+    ]
+
+async def run_agent(task_id: str, api_key: str, model: str, base_url: str):
+    if not api_key:
+        return [gr.update()] * 6 + ["Please provide an API Key."]
+    
+    # 1. Reset
+    out = await reset_env(task_id)
+    
+    # 2. Predict with LLM
+    client_params = {"api_key": api_key}
+    if base_url:
+        client_params["base_url"] = base_url
+    
+    client = AsyncOpenAI(**client_params)
+    
+    user_content = f"{state.obs.task_instruction}\n\n"
+    if state.obs.text_context:
+        user_content += "## Text context\n"
+        for name, text in sorted(state.obs.text_context.items()):
+            user_content += f"### {name}\n{text}\n"
+    if state.obs.numerical_context:
+        user_content += f"\n## Numerical context\n{json.dumps(state.obs.numerical_context)}\n"
+        
+    system_prompt = (
+        "You are a financial analyst assistant. "
+        "Analyze the data and respond EXACTLY as instructed. "
+        "Reply with a single JSON object containing 'prediction' key."
+    )
+    
+    try:
+        completion = await client.chat.completions.create(
+            model=model,
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_content},
+            ],
+            response_format={"type": "json_object"},
+        )
+        response_text = completion.choices[0].message.content or "{}"
+        parsed = json.loads(response_text)
+        prediction = str(parsed.get("prediction", response_text))
+        
+        # 3. Step
+        step_out = await step_env(prediction)
+        
+        # Update UI components
+        # out: [instr, text, num, pred_row, res_row, pred_input, msg]
+        # step_out: [pred_row, res_row]
+        
+        return [
+            out[0], out[1], out[2], 
+            step_out[0], step_out[1], 
+            prediction, 
+            f"Agent used {model}. Raw response: {response_text}"
+        ]
+        
+    except Exception as e:
+        return [out[0], out[1], out[2], out[3], out[4], "", f"Error: {str(e)}"]
+
+# Custom CSS for a premium look
+custom_css = """
+footer {visibility: hidden}
+.container { 
+    max-width: 1100px; 
+    margin: auto; 
+    padding-top: 2rem; 
+    font-family: 'Inter', system-ui, -apple-system, sans-serif;
+}
+.header { text-align: center; margin-bottom: 2rem; }
+.header h1 { font-weight: 800; font-size: 2.5rem; background: linear-gradient(90deg, #ff4d94, #ff85b3); -webkit-background-clip: text; -webkit-text-fill-color: transparent; }
+.header p { color: #666; font-size: 1.1rem; }
+.card { border-radius: 12px; border: 1px solid #eee; background: white; padding: 1.5rem; box-shadow: 0 4px 6px -1px rgba(0,0,0,0.05); }
+.context-box { height: 400px; overflow-y: auto; border: 1px solid #f0f0f0; border-radius: 8px; padding: 1rem; background: #fafafa; }
+"""
+
+with gr.Blocks(css=custom_css, title="Earnings Analyst - OpenEnv") as demo:
+    with gr.Div(elem_classes="container"):
+        with gr.Div(elem_classes="header"):
+            gr.Markdown("# 🏑 Earnings Analyst")
+            gr.Markdown("Interactive environment for financial analysis tasks using [OpenEnv](https://github.com/meta-pytorch/OpenEnv). Evaluate agents or your own analysis on earnings call data.")
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                with gr.Group():
+                    task_select = gr.Dropdown(choices=TASK_IDS, value="sentiment_label", label="Active Task")
+                    reset_btn = gr.Button("🔄 New Episode", variant="primary")
+                    
+                gr.Markdown("### 🤖 Auto-Agent Settings")
+                with gr.Group():
+                    api_key = gr.Textbox(label="OpenAI API Key", type="password", placeholder="sk-...", value=os.environ.get("OPENAI_API_KEY", ""))
+                    model_name = gr.Textbox(label="Model", value="gpt-4o-mini")
+                    base_url = gr.Textbox(label="Base URL (optional)", placeholder="https://api.openai.com/v1", value=os.environ.get("OPENAI_BASE_URL", ""))
+                    agent_btn = gr.Button("🚀 Run LLM Agent", variant="secondary")
+
+            with gr.Column(scale=2):
+                with gr.Tabs():
+                    with gr.TabItem("Observation"):
+                        instr_view = gr.Markdown("### Instruction\n*N/A*")
+                        with gr.Row():
+                            with gr.Column():
+                                gr.Markdown("#### Text Context")
+                                text_view = gr.Markdown(elem_classes="context-box")
+                            with gr.Column():
+                                gr.Markdown("#### Numerical Context")
+                                num_view = gr.Code(label="JSON", language="json", elem_classes="context-box")
+                    
+                    with gr.TabItem("Analysis"):
+                        with gr.Column(visible=False) as prediction_row:
+                            pred_input = gr.Textbox(label="Your Prediction / Analysis Output", placeholder="e.g. bullish, or 0.05")
+                            submit_btn = gr.Button("Submit Analysis", variant="primary")
+                        
+                        result_view = gr.Markdown(visible=False, elem_classes="card")
+                        message_view = gr.Textbox(label="Agent Log / Error Messages", interactive=False)
+
+    # Event handlers
+    reset_btn.click(
+        fn=reset_env,
+        inputs=[task_select],
+        outputs=[instr_view, text_view, num_view, prediction_row, result_view, pred_input, message_view]
+    )
+
+    submit_btn.click(
+        fn=step_env,
+        inputs=[pred_input],
+        outputs=[prediction_row, result_view]
+    )
+
+    agent_btn.click(
+        fn=run_agent,
+        inputs=[task_select, api_key, model_name, base_url],
+        outputs=[instr_view, text_view, num_view, prediction_row, result_view, pred_input, message_view]
+    )
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+# Create the main FastAPI app
+api_app = FastAPI(title="Earnings Analyst API")
+
+# Add CORS middleware
+api_app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Import the environment app
+try:
+    from earnings_analyst.server.app import app as env_app
+except (ImportError, ModuleNotFoundError):
+    from server.app import app as env_app
+
+# Mount the environment API at /api
+api_app.mount("/api", env_app)
+
+# Wrap Gradio with FastAPI
+app = gr.mount_gradio_app(api_app, demo, path="/")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)

pyproject.toml

@@ -16,6 +16,9 @@ dependencies = [
     "openai>=2.31.0",
     "openenv-core[core]>=0.2.2",
     "python-dotenv>=1.2.2",
+    "gradio>=4.0.0",
+    "uvicorn>=0.30.0",
+    "fastapi>=0.111.0",
 ]
 
 [project.optional-dependencies]

scratch/inspect_dataset.py

@@ -1,26 +0,0 @@
-from datasets import load_dataset
-import os
-
-DATASET_ID = "RudrakshNanavaty/earnings-call-data"
-DATASET_FILE = "episodes_press_release_8k.parquet"
-
-print(f"Loading dataset {DATASET_ID} file {DATASET_FILE}...")
-dataset = load_dataset(
-    DATASET_ID,
-    data_files={"train": DATASET_FILE},
-    split="train",
-)
-
-print("\nColumns:")
-print(dataset.column_names)
-
-print("\nFirst row summary:")
-row = dataset[0]
-for k, v in row.items():
-    if v is not None:
-        val_str = str(v)
-        if len(val_str) > 100:
-            val_str = val_str[:100] + "..."
-        print(f"{k}: {val_str}")
-    else:
-        print(f"{k}: None")