feat: agent

app.py

@@ -4,6 +4,7 @@ from process.ocr import perform_raw_ocr, correct_text_with_ai
 from process.interpretation import get_interpretation
 from process.translation import get_translaton
 from process.gradio_css import CUSTOM_CSS
+from process.agent import AutomatedAnalysisAgent
 
 
 MISTRAL_API_KEY = ""
@@ -144,6 +145,20 @@ def translation_workflow(text: str, target_language: str, gemini_key):
 		yield "not implemented yet"
 
 
+def agent_workflow(text: str, prof_language: str, mistral_key: str, gemini_key: str):
+    if not mistral_key or not gemini_key:
+        return "Error: Both Mistral and Gemini API keys are required."
+    if not text or not text.strip():
+        return "Error: Input text is empty."
+    
+    try:
+        agent = AutomatedAnalysisAgent(mistral_key=mistral_key, gemini_key=gemini_key)
+        result = agent.run(text, prof_language=prof_language)
+        return result
+    except Exception as e:
+        return f"An error occurred in the agent workflow: {e}"
+
+
 with gr.Blocks(theme=gr.themes.Monochrome(), css=CUSTOM_CSS) as demo:
 	gr.Markdown("# 📚 LogosAI - Intensive Reading in Any Language", elem_classes=["section-header"])
 
@@ -220,6 +235,27 @@ with gr.Blocks(theme=gr.themes.Monochrome(), css=CUSTOM_CSS) as demo:
 		outputs=[text_display, text_markdown]
 	)
 
+	# --- Agent ---
+	with gr.Tab("✨ Agent"):
+		gr.Markdown("### Automated Analysis")
+		with gr.Row():
+			with gr.Column(scale=1):
+				agent_prof_language_selector = gr.Dropdown(["AR", "DE", "ES", "EN", "FR", "IT", "JA", "RU", "ZH"], label="Prof's Language", value="EN")
+				agent_run_button = gr.Button("Run Automated Analysis", variant="primary")
+			with gr.Column(scale=2):
+				gr.Markdown("### Agent Result")
+				agent_output = gr.Markdown(
+					value="*Agent analysis will appear here...*\n\n",
+					label="Agent Result"
+				)
+		
+		agent_run_button.click(
+			fn=agent_workflow,
+			inputs=[text_display, agent_prof_language_selector, mistral_api, gemini_api],
+			outputs=agent_output
+		)
+
+
 	# --- Text Interpertation ---
 	with gr.Tab("🎓 Interpretation"):
 		gr.Markdown("### Configure Interpretation Settings")

process/agent.py

@@ -0,0 +1,102 @@
+
+import json
+from google import genai
+from google.genai import types
+from process.ocr import correct_text_with_ai
+from process.interpretation import get_interpretation
+
+AGENT_SYS_PROMPT = """
+You are a highly intelligent text analysis agent. Your sole purpose is to analyze a given text and return a JSON object with three keys: "language", "genre", and "correction_needed".
+
+1.  **"language"**: Identify the primary language of the text. The value must be one of the following ISO 639-1 codes: ["AR", "DE", "ES", "EN", "FR", "IT", "JA", "RU", "ZH"].
+
+2.  **"genre"**: Analyze the text's style, tone, and content to determine its genre. The value must be one of the following strings: ["General", "News", "Philosophy", "Narrative", "Poem", "Paper"].
+    *   "News": Reports on current events.
+    *   "Philosophy": Discusses fundamental questions about existence, knowledge, values, reason, mind, and language.
+    *   "Narrative": Tells a story.
+    *   "Poem": Uses aesthetic and rhythmic qualities of language.
+    *   "Paper": A formal academic or scientific paper.
+    *   "General": Any text that does not fit neatly into the other categories.
+
+3.  **"correction_needed"**: Determine if the text contains obvious OCR errors, typos, or significant grammatical mistakes that require correction. The value must be a boolean (`true` or `false`). Set it to `true` if you see scrambled words, weird symbols, or frequent misspellings.
+
+Your response MUST be a single, valid JSON object and nothing else. Do not add any explanations, comments, or markdown formatting.
+
+Example input text:
+"Teh qick brown fox juumps over teh lazy dog. It was a sunny day in london."
+
+Example output:
+{
+  "language": "EN",
+  "genre": "Narrative",
+  "correction_needed": true
+}
+"""
+
+class AutomatedAnalysisAgent:
+    def __init__(self, mistral_key: str, gemini_key: str):
+        if not mistral_key or not gemini_key:
+            raise ValueError("Both Mistral and Gemini API keys are required.")
+        self.mistral_key = mistral_key
+        self.gemini_key = gemini_key
+        self.genai_client = genai.Client(api_key=self.gemini_key)
+
+    def _get_analysis_directives(self, text: str) -> dict:
+        """
+        Analyzes the text to determine language, genre, and if correction is needed.
+        """
+        try:
+            response = self.genai_client.models.generate_content(
+                model="gemini-2.5-flash",
+                config=types.GenerateContentConfig(
+                    system_instruction=AGENT_SYS_PROMPT,
+                    temperature=0.0,
+                    response_mime_type="application/json",
+                ),
+                contents=[text]
+            )
+            directives = json.loads(response.text)
+            # Basic validation
+            if "language" not in directives or "genre" not in directives or "correction_needed" not in directives:
+                raise ValueError("Invalid JSON structure from analysis model.")
+            return directives
+        except Exception as e:
+            # Fallback or error handling
+            print(f"Error during analysis: {e}")
+            # Provide a default, safe directive
+            return {"language": "EN", "genre": "General", "correction_needed": True}
+
+    def run(self, text: str, prof_language: str = "EN") -> str:
+        """
+        Runs the full automated analysis and interpretation workflow.
+        """
+        if not text or not text.strip():
+            return "Error: Input text is empty."
+
+        # 1. Get analysis directives from the agent's brain
+        directives = self._get_analysis_directives(text)
+        
+        processed_text = text
+        # 2. Conditionally apply AI correction
+        if directives.get("correction_needed", False):
+            try:
+                processed_text = correct_text_with_ai(text, self.mistral_key)
+            except Exception as e:
+                print(f"Error during AI correction: {e}")
+                # Proceed with original text if correction fails
+                processed_text = text
+
+        # 3. Get the final interpretation
+        try:
+            interpretation = get_interpretation(
+                genre=directives.get("genre", "General"),
+                api_key=self.gemini_key,
+                text=processed_text,
+                learn_language=directives.get("language", "EN"),
+                prof_language=prof_language
+            )
+            return interpretation
+        except Exception as e:
+            print(f"Error during interpretation: {e}")
+            return f"An error occurred during the final interpretation step: {e}"
+