Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,93 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import os
+from google import genai
+from google.genai import types
 
-"""
-# Welcome to Streamlit!
+# --- Configuration & Styling ---
+st.set_page_config(page_title="Gemini 3: Hypothesis Engine", layout="wide")
+st.title("🔬 Advanced Scientific Hypothesis Engine")
+st.caption("Powered by Gemini 3 Pro Reasoning & Action Loops")
+
+# SECURE API KEY: Add this in Hugging Face "Settings > Secrets"
+API_KEY = os.environ.get("GOOGLE_API_KEY")
+
+if not API_KEY:
+    st.error("Please add your GOOGLE_API_KEY to the Hugging Face Space Secrets.")
+    st.stop()
 
-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).
+# --- Initialize Gen AI Client ---
+client = genai.Client(api_key=API_KEY)
 
-In the meantime, below is an example of what you can do with just a few lines of code:
+# --- Define Advanced System Instructions ---
+SYSTEM_INSTRUCTIONS = """
+You are a Senior Scientific Discovery Agent specializing in cross-disciplinary synthesis.
+Your core objective: Find contradictions, missing links, or novel hypotheses in massive research datasets.
+
+STRATEGIC PROTOCOL:
+1. ANALYSIS: Scan the 1M token context for conflicting claims between papers.
+2. PLANNING: Explicitly state your reasoning path before taking any action.
+3. VERIFICATION: Use the 'code_execution' tool to run Python simulations or statistical checks.
+4. GROUNDING: Use 'google_search' to verify if your discovery is already public.
+5. PERSISTENCE: If a tool fails, analyze the error and try a different Python approach.
+DO NOT provide medical diagnoses. Focus on chemistry, physics, and materials science.
 """
 
-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])),
-    ))
+# --- Stateful Session Management ---
+if "chat" not in st.session_state:
+    # Official SDK handles Thought Signatures automatically in Chat sessions
+    st.session_state.chat = client.chats.create(
+        model="gemini-3-pro-preview",
+        config=types.GenerateContentConfig(
+            system_instruction=SYSTEM_INSTRUCTIONS,
+            thinking_config=types.ThinkingConfig(
+                include_thoughts=True, 
+                thinking_level=types.ThinkingLevel.HIGH  # Mandatory for Marathon Agents
+            ),
+            tools=[
+                types.Tool(google_search=types.GoogleSearchRetrieval()),
+                types.Tool(code_execution=types.ToolCodeExecution())
+            ],
+            temperature=1.0  # Gemini 3 reasoning is optimized for 1.0
+        )
+    )
+    st.session_state.messages = []
+
+# --- UI Sidebar: Multi-Paper Ingestion ---
+with st.sidebar:
+    st.header("Research Corpus")
+    uploaded_files = st.file_uploader("Upload PDFs (Max 1M Tokens)", type="pdf", accept_multiple_files=True)
+    if st.button("Reset Lab State"):
+        st.session_state.chat = None # Resetting will trigger re-initialization
+        st.session_state.messages = []
+        st.rerun()
+
+# --- Main Interaction Loop ---
+for msg in st.session_state.messages:
+    with st.chat_message(msg["role"]):
+        st.markdown(msg["content"])
+
+if prompt := st.chat_input("Enter your research objective..."):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        # We use st.status to show the "Thought Signatures" and Action Loops live
+        with st.status("Agent Reasoning...", expanded=True) as status:
+            response = st.session_state.chat.send_message(prompt)
+            
+            # 1. Display Internal Reasoning (Thought Summary)
+            if response.candidates[0].thought_summary:
+                st.info(f"**Thought Signature Path:**\n{response.candidates[0].thought_summary}")
+            
+            # 2. Display Action Loop: Code Execution & Search
+            for part in response.candidates[0].content.parts:
+                if part.executable_code:
+                    st.code(part.executable_code.code, language="python", label="Agent-Generated Script")
+                if part.code_execution_result:
+                    st.success(f"Execution Output: {part.code_execution_result.output}")
+            
+            status.update(label="Discovery Finalized", state="complete")
+
+        st.markdown(response.text)
+        st.session_state.messages.append({"role": "assistant", "content": response.text})