Update app.py

app.py

@@ -3,86 +3,91 @@ import os
 from google import genai
 from google.genai import types
 
-# 1. Setup API Client
-# On Hugging Face, add your key to "Settings > Secrets"
-api_key = os.environ.get("GOOGLE_API_KEY")
-client = genai.Client(api_key=api_key)
-
-st.set_page_config(page_title="Gemini 3 Hypothesis Engine", layout="wide")
+# --- 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")
 
-# 2. Initialize Stateful Chat History (Including Thought Signatures)
-if "messages" not in st.session_state:
-    st.session_state.messages = []
+# SECURE API KEY: Add this in Hugging Face "Settings > Secrets"
+API_KEY = os.environ.get("GOOGLE_API_KEY")
 
-# 3. Model Configuration (Action Era Settings)
-config = types.GenerateContentConfig(
-    thinking_config=types.ThinkingConfig(
-        include_thoughts=True, 
-        thinking_level="HIGH"  # Forces deep reasoning
-    ),
-    tools=[
-        types.Tool(google_search=types.GoogleSearch()), # Real-time verification
-        types.Tool(code_execution=types.CodeExecution()) # Math/Logic verification
-    ],
-    system_instruction="""You are a Senior Scientific Research Agent. 
-    Your goal is to find contradictions and novel links across multiple research papers.
-    When a user asks a question, scan the provided context, propose a hypothesis, 
-    and use your Python tool to verify the mathematical feasibility of your theory."""
-)
+if not API_KEY:
+    st.error("Please add your GOOGLE_API_KEY to the Hugging Face Space Secrets.")
+    st.stop()
 
-# 4. UI: Sidebar for Paper Uploads
-with st.sidebar:
-    st.header("Research Materials")
-    uploaded_files = st.file_uploader("Upload PDFs", type="pdf", accept_multiple_files=True)
-    if st.button("Clear History"):
-        st.session_state.messages = []
-        st.rerun()
+# --- Initialize Gen AI Client ---
+client = genai.Client(api_key=API_KEY)
+
+# --- 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.
+"""
 
-# 5. The Development Loop
-def process_query(user_input):
-    # Add user message to history
-    st.session_state.messages.append(types.Content(role="user", parts=[types.Part.from_text(user_input)]))
-    
-    # Generate content using the full history (this preserves the state)
-    response = client.models.generate_content(
+# --- 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",
-        contents=st.session_state.messages,
-        config=config
+        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
+        )
     )
-    
-    # Save the model's response (this contains the mandatory thought_signature)
-    st.session_state.messages.append(response.candidates[0].content)
-    return response
+    st.session_state.messages = []
 
-# 6. Streamlit Chat Interface
+# --- 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:
-    role = "user" if msg.role == "user" else "assistant"
-    with st.chat_message(role):
-        # We only display text parts to the user
-        for part in msg.parts:
-            if part.text:
-                st.markdown(part.text)
+    with st.chat_message(msg["role"]):
+        st.markdown(msg["content"])
 
-if prompt := st.chat_input("Enter a scientific mystery to solve..."):
+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"):
-        with st.status("Thinking & Verifying...", expanded=True) as status:
-            response = process_query(prompt)
+        # 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)
             
-            # SHOWING THE "ADVANCED" LEVEL: Display the AI's internal reasoning
+            # 1. Display Internal Reasoning (Thought Summary)
             if response.candidates[0].thought_summary:
-                st.info(f"**Reasoning Path:** {response.candidates[0].thought_summary}")
+                st.info(f"**Thought Signature Path:**\n{response.candidates[0].thought_summary}")
             
-            # Show if the AI used tools (Code/Search)
+            # 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")
+                    st.code(part.executable_code.code, language="python", label="Agent-Generated Script")
                 if part.code_execution_result:
-                    st.success(f"Execution Result: {part.code_execution_result.output}")
+                    st.success(f"Execution Output: {part.code_execution_result.output}")
             
-            status.update(label="Hypothesis Ready!", state="complete")
-        
-        st.markdown(response.text)
+            status.update(label="Discovery Finalized", state="complete")
+
+        st.markdown(response.text)
+        st.session_state.messages.append({"role": "assistant", "content": response.text})