Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,124 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+import os
+import torch
 import streamlit as st
+from typing import Literal
+from tavily import TavilyClient
+from deepagents import create_deep_agent
+from langchain.callbacks.streamlit import StreamlitCallbackHandler
+from langgraph.store.memory import InMemoryStore
+from langchain.chat_models import init_chat_model
+from langchain_huggingface import ChatHuggingFace, HuggingFacePipeline
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+
+# --- Sidebar: API Keys ---
+st.sidebar.header("🔑 API Keys")
+
+tavily_api_key = st.sidebar.text_input("Tavily API Key", type="password", placeholder="sk-tavily-XXXX")
+openai_api_key = st.sidebar.text_input("OpenAI API Key", type="password", placeholder="sk-openai-XXXX")
+claude_api_key = st.sidebar.text_input("Anthropic API Key", type="password", placeholder="sk-ant-XXXX")
+gemini_api_key = st.sidebar.text_input("Google Gemini API Key", type="password", placeholder="AIzaSyXXXX")
+
+# --- Sidebar: Model Selection ---
+st.sidebar.header("🧠 Choose Model Provider")
+provider = st.sidebar.selectbox(
+    "Select provider:",
+    ["OpenAI", "Claude", "Gemini", "Hugging Face"],
+    index=1,
+)
+
+# --- Editable system prompt ---
+st.sidebar.header("🧩 Research Instructions")
+research_instructions = st.sidebar.text_area(
+    "System prompt for the agent:",
+    value=(
+        "You are an expert code researcher. Your job is to conduct thorough research, analyze complex information, "
+        "and write a concise, well-structured report.\n\n"
+        "You have access to an internet search tool as your primary method of gathering external information.\n"
+        "## `internet_search`\n"
+        "Use this function to query the web. Specify a topic, max results, and whether to include raw content."
+    ),
+    height=200,
+)
+
+# --- Store & Tavily Client ---
+if not tavily_api_key:
+    st.warning("Please enter your Tavily API key in the left menu to start the agent.")
+    st.stop()
+
+store = InMemoryStore()
+tavily_client = TavilyClient(api_key=tavily_api_key)
+
+# --- Web search tool ---
+def internet_search(
+    query: str,
+    max_results: int = 5,
+    topic: Literal["general", "news", "finance"] = "general",
+    include_raw_content: bool = False,
+):
+    return tavily_client.search(
+        query,
+        max_results=max_results,
+        include_raw_content=include_raw_content,
+        topic=topic,
+    )
+
+# --- Model Loader ---
+def load_model(provider: str):
+    if provider == "OpenAI":
+        if not openai_api_key:
+            st.error("Please enter your OpenAI API key.")
+            st.stop()
+        os.environ["OPENAI_API_KEY"] = openai_api_key
+        return init_chat_model(model="openai:gpt-5")
+
+    elif provider == "Claude":
+        if not claude_api_key:
+            st.error("Please enter your Anthropic API key.")
+            st.stop()
+        os.environ["ANTHROPIC_API_KEY"] = claude_api_key
+        return init_chat_model(model="claude-sonnet-4-5-20250929")
+
+    elif provider == "Gemini":
+        if not gemini_api_key:
+            st.error("Please enter your Google Gemini API key.")
+            st.stop()
+        os.environ["GOOGLE_API_KEY"] = gemini_api_key
+        return init_chat_model(model="gemini-2.5-pro")
+
+    elif provider == "Hugging Face":
+        device = 0 if torch.cuda.is_available() else -1
+        llm = HuggingFacePipeline.from_model_id(
+            model_id="EpistemeAI/Episteme-gptoss-20b-RL",
+            task="text-generation",
+            device=device,
+            pipeline_kwargs={"max_new_tokens": 8000},
+        )
+        return ChatHuggingFace(llm=llm)
+
+    else:
+        st.error("Unknown provider selected.")
+        st.stop()
+
+# --- Initialize Model ---
+model = load_model(provider)
+
+# --- Create the Deep Agent ---
+agent = create_deep_agent(
+    store=store,
+    use_longterm_memory=True,
+    model=model,
+    tools=[internet_search],
+    system_prompt=research_instructions,
+)
+
+# --- Streamlit Chat UI ---
+st.title("🧠 Deep Research Agent")
+st.caption(f"Running on: **{provider}** backend")
+
+if prompt := st.chat_input("Ask your research question..."):
+    st.chat_message("user").write(prompt)
+    with st.chat_message("assistant"):
+        st_callback = StreamlitCallbackHandler(st.container())
+        response = agent.invoke({"input": prompt}, {"callbacks": [st_callback]})
+        output = response.get("output", str(response))
+        st.write(output)