Update app.py

app.py

@@ -1,147 +1,20 @@
 import os
-import gradio as gr
-from duckduckgo_search import DDGS
-from gradio_client import Client
-import time
+import tempfile
+import importlib.util
 
-def generate_search_queries(topic):
-    """Generate optimized search queries."""
-    prompt = f"""
-    <Instructions>
-    You are an AI research strategist that generates optimized search queries for investigating complex topics. When I provide a <topic>, create 10-15 search terms/phrases that would effectively discover relevant information through search engines and academic databases.
-
-    Rules for query generation:
-    1. Include 3 levels of specificity: broad conceptual terms, mid-range topic phrases, niche technical terms
-    2. Cover multiple research angles: definitions, controversies, applications, case studies, trends
-    3. Use both quoted exact-match phrases and natural language questions
-    4. Include synonyms and variant terminology
-    5. Avoid duplicate concepts - each query must target distinct information
-    6. Order queries from general to specific
-
-    Example response format:
-    <search_queries>
-    <query>[1] "generative AI" AND intellectual property</query>
-    <query>[2] Training data sourcing legality LLM</query>
-    ...
-    </search_queries>
-
-    Now process this topic:
-    <topic>{topic}</topic>
-    </Instructions>
-    """
-    try:
-        response = DDGS().chat(prompt, model='o3-mini')
-        queries = []
-        
-        for part in response.split("</query>"):
-            if "<query>" in part:
-                query_text = part.split("<query>")[-1].strip()
-                if query_text:
-                    clean_query = query_text.split("] ", 1)[-1] if "] " in query_text else query_text
-                    queries.append(clean_query)
-        
-        if not queries:
-            return [f"{topic} historical analysis", 
-                    f"{topic} primary sources",
-                    f"{topic} geopolitical impact"]
-            
-        return queries[:15]
+def load_app():
+    code = os.getenv("APP_CODE")
+    if not code:
+        raise RuntimeError("No application code found")
     
-    except Exception as e:
-        print(f"Error generating queries: {str(e)}")
-        return [topic]
-
-def conduct_research(query):
-    """Conduct deep research on a single query"""
-    client = Client("m-ric/open_Deep-Research")
-    client.predict(query, api_name="/log_user_message")
-    research_data = client.predict([], api_name="/interact_with_agent")
+    with tempfile.NamedTemporaryFile(suffix=".py", delete=False) as f:
+        f.write(code.encode('utf-8'))
+        tmp_name = f.name
     
-    for msg in reversed(research_data):
-        if "Final answer:" in msg['content']:
-            return msg['content'].split("Final answer:")[-1].strip()
-    return "No conclusive information found"
-
-def synthesize_results(original_query, queries, findings):
-    """Synthesize research findings into final summary"""
-    synthesis_prompt = f"""
-    <Inputs>
-    Original Query: {original_query}
-    Research Queries: {queries}
-    Research Findings: {findings}
-    </Inputs>
-
-    <Instructions>
-    You are an analytical research synthesizer. Merge these findings into one cohesive summary:
-
-    1. Start with 1 paragraph overview
-    2. Bullet points of key findings (minimum 5)
-    3. 1 paragraph synthesis connecting findings to original query
-    4. "Additional Notes" section for peripheral but useful details
-
-    Rules:
-    - Include EVERY relevant data point
-    - Natural conversational English but professional
-    - No markdown formatting
-    - Keep paragraphs under 5 sentences
-
-    Example structure:
-    "Three separate analyses concur... [specific data]... This suggests... [connection to query]..."
-
-    Begin by confirming understanding of the core query, then proceed with synthesis.
-    </Instructions>
-    """
-    
-    synthesizer = Client("MiniMaxAI/MiniMax-Text-01")
-    return synthesizer.predict(
-        message=synthesis_prompt,
-        max_tokens=1000,
-        temperature=0.1,
-        top_p=0.9,
-        api_name="/chat"
-    )
-
-def deep_research_agent(topic):
-    queries = generate_search_queries(topic)
-    print(f"🔍 Generated {len(queries)} research queries")
-    
-    findings = []
-    for i, query in enumerate(queries, 1):
-        print(f"⏳ Researching query {i}/{len(queries)}: {query}")
-        findings.append(conduct_research(query))
-        time.sleep(1)
-    
-    print("🧠 Synthesizing findings...")
-    return synthesize_results(topic, queries, findings)
-
-def create_interface():
-    with gr.Blocks(analytics_enabled=False) as app:
-        gr.Markdown("# Stealth Research Assistant")
-        with gr.Row():
-            topic_input = gr.Textbox(label="Research Topic", max_lines=1)
-            submit_btn = gr.Button("Start Analysis", variant="primary")
-        
-        status = gr.Textbox(label="Operation Status", value="Ready", interactive=False)
-        output = gr.Textbox(label="Final Report", lines=15, interactive=False)
-        
-        @submit_btn.click(inputs=topic_input, outputs=[output, status], api_name=False)
-        def execute_analysis(topic):
-            try:
-                yield ["", "Analyzing topic..."]
-                result = deep_research_agent(topic)
-                yield [result, "Completed"]
-            except Exception as e:
-                yield ["", f"Error: {str(e)[:200]}"]
-
-    return app
-
-def launch():
-    interface = create_interface()
-    interface.queue().launch(
-        server_name=os.getenv("SERVER_HOST", "127.0.0.1"),
-        server_port=int(os.getenv("SERVER_PORT", "7860")),
-        show_api=False
-    )
+    spec = importlib.util.spec_from_file_location("hidden_app", tmp_name)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    module.launch()
 
 if __name__ == "__main__":
-    launch()
+    load_app()