update temperature

app.py

@@ -1,49 +1,61 @@
 import os, base64, json, uuid, torch, gradio as gr
 from pathlib import Path
-from src.llm.chat import FunctionCallingChat     
+from src.llm.chat import FunctionCallingChat
 
-chatbot = FunctionCallingChat()                  
+chatbot = FunctionCallingChat()          
+chatbot.temperature = 0.7            
 
 def image_to_base64(image_path: str):
     with open(image_path, "rb") as f:
         return base64.b64encode(f.read()).decode("utf-8")
 
 def save_uploaded_image(pil_img) -> Path:
+    """Save PIL image to ./static and return its path."""
     Path("static").mkdir(exist_ok=True)
     filename = f"upload_{uuid.uuid4().hex[:8]}.png"
     path = Path("static") / filename
     pil_img.save(path)
     return path
 
-def inference(pil_img, prompt, task):
+def inference(pil_img, prompt, task, temperature):
     if pil_img is None:
         return "❗ Please upload an image first."
 
-    img_path = save_uploaded_image(pil_img)
+    img_path = save_uploaded_image(pil_img)          
+    chatbot.temperature = temperature               
 
+    # build user message
     if task == "Detection":
         user_msg = f"Please detect objects in the image '{img_path}'."
     elif task == "Segmentation":
         user_msg = f"Please segment objects in the image '{img_path}'."
-    else:  
+    else:
         prompt = prompt.strip() or "Analyse this image."
         user_msg = f"{prompt} (image: '{img_path}')"
 
-    out = chatbot(user_msg)
-    txt = (
-        "### 🔧 Raw tool-call \n"
-        f"{out['raw_tool_call']}\n\n"
-        "### 📦 Tool results\n"
-        f"{json.dumps(out['results'], indent=2)}"
-    )
-    return txt
-
+    try:
+        out = chatbot(user_msg)                      
+        txt = (
+            "### 🔧 Raw tool-call\n"
+            f"{out['raw_tool_call']}\n\n"
+            "### 📦 Tool results\n"
+            f"{json.dumps(out['results'], indent=2)}"
+        )
+        return txt
+    finally:
+        # 4️⃣ always delete the temp image
+        try:
+            img_path.unlink(missing_ok=True)
+        except Exception:
+            pass   # if deletion fails we just move on
+
+# ────────────────────────────  UI  ────────────────────────────
 def create_header():
     with gr.Row():
         with gr.Column(scale=1):
             logo_base64 = image_to_base64("static/aivn_logo.png")
             gr.HTML(
-                f"""<img src="data:image/png;base64,{logo_base64}" 
+                f"""<img src="data:image/png;base64,{logo_base64}"
                         alt="Logo"
                         style="height:120px;width:auto;margin-right:20px;margin-bottom:20px;">"""
             )
@@ -63,7 +75,6 @@ def create_header():
 """
             )
 
-
 def create_footer():
     footer_html = """
 <style>
@@ -80,7 +91,6 @@ def create_footer():
 """
     return gr.HTML(footer_html)
 
-
 custom_css = """
 .gradio-container {min-height:100vh;}
 .content-wrap {padding-bottom:60px;}
@@ -90,6 +100,7 @@ custom_css = """
 .full-width-btn:hover {background:linear-gradient(45deg,#FF5252,#3CB4AC)!important;}
 """
 
+# ────────────────────────────  Blocks  ─────────────────────────
 with gr.Blocks(css=custom_css) as demo:
     create_header()
 
@@ -97,9 +108,13 @@ with gr.Blocks(css=custom_css) as demo:
         with gr.Column(scale=3):
             upload_image = gr.Image(label="Upload image", type="pil")
             prompt_input = gr.Textbox(label="Optional prompt", placeholder="e.g. Detect cats only")
-            task_choice  = gr.Radio(
-                ["Auto", "Detection", "Segmentation"], value="Auto", label="Task"
-            )
+            task_choice  = gr.Radio(["Auto", "Detection", "Segmentation"],
+                                     value="Auto", label="Task")
+
+            # NEW temperature slider
+            temp_slider  = gr.Slider(minimum=0.1, maximum=1.5, step=0.1,
+                                     value=0.7, label="Temperature (sampling)")
+
             submit_btn   = gr.Button("Run 🔧", elem_classes="full-width-btn")
 
         with gr.Column(scale=4):
@@ -107,7 +122,7 @@ with gr.Blocks(css=custom_css) as demo:
 
         submit_btn.click(
             inference,
-            inputs=[upload_image, prompt_input, task_choice],
+            inputs=[upload_image, prompt_input, task_choice, temp_slider],
             outputs=output_text,
         )