gradio

README.md

@@ -5,7 +5,7 @@ colorFrom: yellow
 colorTo: purple
 sdk: gradio
 sdk_version: 5.0.1
-app_file: app.py
+app_file: agentic_implementation/gradio_ag.py
 pinned: false
 short_description: Answer any questions you have about the content of your mail
 ---

agentic_implementation/gradio_ag.py

@@ -0,0 +1,102 @@
+import gradio as gr
+import json
+from typing import Any, Dict, List, Tuple
+
+from re_act import (
+    get_plan_from_llm,
+    think,
+    act,
+    store_name_email_mapping,
+    extract_sender_info,
+    client,
+)
+from logger import logger  # Assumes logger is configured
+from schemas import PlanStep
+
+# Maintain persistent session results
+session_results: Dict[str, Any] = {}
+
+def respond(
+    message: str,
+    history: List[Tuple[str, str]],
+    system_message: str,
+    max_tokens: int,
+    temperature: float
+) -> str:
+    logger.info("Gradio agent received message: %s", message)
+    full_response = ""
+
+    try:
+        # Step 1: Generate plan
+        plan = get_plan_from_llm(message)
+        logger.debug("Generated plan: %s", plan)
+        full_response += "📌 **Plan**:\n"
+        for step in plan.plan:
+            full_response += f"- {step.action}\n"
+        full_response += "\n"
+
+        results = {}
+
+        # Step 2: Execute steps
+        for step in plan.plan:
+            if step.action == "done":
+                full_response += "✅ Plan complete.\n"
+                break
+
+            should_run, updated_step, user_prompt = think(step, results, message)
+
+            # Ask user for clarification if needed
+            if user_prompt:
+                full_response += f"❓ {user_prompt} (Please respond with an email)\n"
+                return full_response  # wait for user
+
+            if not should_run:
+                full_response += f"⏭️ Skipping `{step.action}`\n"
+                continue
+
+            try:
+                output = act(updated_step)
+                results[updated_step.action] = output
+                full_response += f"🔧 Ran `{updated_step.action}` → {output}\n"
+            except Exception as e:
+                logger.error("Error running action '%s': %s", updated_step.action, e)
+                full_response += f"❌ Error running `{updated_step.action}`: {e}\n"
+                break
+
+        # Step 3: Summarize results
+        try:
+            summary_rsp = client.chat.completions.create(
+                model="gpt-4o-mini",
+                temperature=temperature,
+                max_tokens=max_tokens,
+                messages=[
+                    {"role": "system", "content": "Summarize these results for the user in a friendly way."},
+                    {"role": "assistant", "content": json.dumps(results)}
+                ],
+            )
+            summary = summary_rsp.choices[0].message.content
+            full_response += "\n📋 **Summary**:\n" + summary
+        except Exception as e:
+            logger.error("Summary generation failed: %s", e)
+            full_response += "\n❌ Failed to generate summary."
+
+    except Exception as e:
+        logger.exception("Unhandled error in agent: %s", e)
+        full_response += f"\n❌ Unexpected error: {e}"
+
+    return full_response
+
+
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(label="System message", value="You are an email assistant agent."),
+        gr.Slider(label="Max tokens", minimum=64, maximum=2048, value=512, step=1),
+        gr.Slider(label="Temperature", minimum=0.0, maximum=1.5, value=0.7, step=0.1),
+    ],
+    title="📬 Email Agent",
+    description="Ask me anything related to your email tasks!"
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -7,3 +7,5 @@ python-dateutil
 beautifulsoup4
 python-dotenv
 pydantic[email]
+gradio
+loguru