various changes

.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+

app.py

@@ -27,7 +27,7 @@ client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 override_header = os.getenv("OVERRIDE_HEADER")
 login_password = os.getenv("LOGIN_PASSWORD")
 
-def compare_cameras(model_name: str, prompt: str, camera1_specs: CameraSpecs, camera2_specs: CameraSpecs) -> SimpleComparisonSummary:
+def compare_cameras(model_name: str, prompt: str, camera1_specs: CameraSpecs, camera2_specs: CameraSpecs, verbosity: str = "Low", reasoning_level: str = "Minimal") -> SimpleComparisonSummary:
     """
     Compares two product specifications using OpenAI and returns a structured summary.
     """
@@ -43,20 +43,43 @@ def compare_cameras(model_name: str, prompt: str, camera1_specs: CameraSpecs, ca
     {camera2_specs.model_dump_json(indent=2)}
     """
 
+    # Map verbosity and reasoning level to Responses API parameters
+    verbosity_map = {
+        "Low": (600, "Keep the response concise and focused on essentials."),
+        "Medium": (1200, "Provide a balanced level of detail."),
+        "High": (2000, "Be thorough and include more detail and nuance."),
+    }
+    max_tokens, verbosity_hint = verbosity_map.get(verbosity, (600, "Keep the response concise."))
+
+    reasoning_map = {"Minimal": "low", "Medium": "medium", "High": "high"}
+    reasoning_effort = reasoning_map.get(reasoning_level, "low")
+
     try:
-        response = client.chat.completions.create(
-            model= model_name,  # You can use a different model if preferred
-            messages=[
-                {"role": "system", "content": "You are a helpful assistant that compares camera specifications and outputs JSON."},
-                {"role": "user", "content": prompt}
+        response = client.responses.create(
+            model=model_name,
+            input=[
+                {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant that compares camera specifications and outputs JSON. " + verbosity_hint}]},
+                {"role": "user", "content": [{"type": "text", "text": prompt}]},
             ],
-             # Instruct the model to return a JSON object
-             response_format={"type": "json_object"}
+            response_format={
+                "type": "json_schema",
+                "json_schema": {
+                    "name": "SimpleComparisonSummary",
+                    "schema": SimpleComparisonSummary.model_json_schema(),
+                    "strict": True,
+                },
+            },
+            max_output_tokens=max_tokens,
+            reasoning={"effort": reasoning_effort},
         )
-        # Parse the JSON string from the response
-        print(response)
-        summary_data = json.loads(response.choices[0].message.content)
-        # Validate and return the data using the Pydantic model
+
+        try:
+            json_text = response.output_text  # type: ignore
+        except Exception:
+            # Fallback to explicit path
+            json_text = response.output[0].content[0].text  # type: ignore
+
+        summary_data = json.loads(json_text)
         return SimpleComparisonSummary(**summary_data)
     except Exception as e:
         print(f"An error occurred: {e}")
@@ -106,9 +129,8 @@ def extract_group_details(data):
   return extracted_data
 
 
-model_list = ["gpt-4.1-nano", "gpt-4.1-mini", "gpt-4.1", "gpt-5-nano", "gpt-5-mini"]
-default_prompt = """
-Follow this exact markdown heading arrangement in your response:
+model_list = ["gpt-5-nano", "gpt-5-mini", "gpt-5"]
+default_prompt = """Follow this exact markdown heading arrangement in your response:
  
 ### Which Is Right for You?
 Provide two clear, persuasive paragraphs—one advocating for product A and the other for product B—each tailored to a distinct use case or preference based on the given specs.
@@ -121,10 +143,9 @@ List and explain the most significant shared features of the products. This sect
  
 If any specification for either product is missing or unavailable, note this clearly at the appropriate place in your summary (e.g., '[Product Name] specification not provided'). Do not mention pricing.
  
-Maintain the prescribed section order: Which Is Right for You, Main Differences, and Key Similarities. Ensure every section is included, even if you must state that information is missing.
-"""
+Maintain the prescribed section order: Which Is Right for You, Main Differences, and Key Similarities. Ensure every section is included, even if you must state that information is missing."""
 
-def compare_cameras_gui(model_name, prompt, sku1, sku2):
+def compare_cameras_gui(model_name, verbosity, reasoning_level, prompt, sku1, sku2):
     """
     Gradio interface function to compare cameras based on SKU numbers and model name.
     """
@@ -138,7 +159,7 @@ def compare_cameras_gui(model_name, prompt, sku1, sku2):
     camera_b = CameraSpecs(model=specs_b['model'], specs=specs_b['specs'])
     names = [specs_a['model'], specs_b['model']]
 
-    summary = compare_cameras(model_name, prompt, camera_a, camera_b)
+    summary = compare_cameras(model_name, prompt, camera_a, camera_b, verbosity=verbosity, reasoning_level=reasoning_level)
 
     if summary:
         # Format the output for display in Gradio using more standard markdown syntax
@@ -152,29 +173,33 @@ def compare_cameras_gui(model_name, prompt, sku1, sku2):
 
 
 default_response_filler="""
-
 ## Output will display here,
 
 ### Add 2 sku numbers to compare.
 
 There is some delay before loading the output, please be patient.
 """
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=compare_cameras_gui,
-    inputs=[gr.Dropdown(choices=model_list, label="Select OpenAI Model", value=model_list[0]),
-            gr.Textbox(label="Prompt", value=default_prompt),
-            gr.Textbox(label="Enter SKU for Product 1"),
-            gr.Textbox(label="Enter SKU for Product 2")],
-    outputs=gr.Markdown(label="Comparison Result", value=default_response_filler),
-    title="Product Comparison Tool",
-    #flagging_mode='never',
-    description="Enter two B&H Photo SKU numbers to compare two products."
-)
+with gr.Blocks(title="Product Comparison Tool") as demo:
+    gr.Markdown("Enter two B&H Photo SKU numbers to compare two products.")
+    with gr.Row():
+        model_dd = gr.Dropdown(choices=model_list, label="Select OpenAI Model", value="gpt-5-nano")
+        verbosity_dd = gr.Dropdown(choices=["Low", "Medium", "High"], label="Verbosity", value="Low")
+        reasoning_dd = gr.Dropdown(choices=["Minimal", "Low", "Medium", "High"], label="Reasoning Level", value="Minimal")
+    prompt_tb = gr.Textbox(label="System Prompt", value=default_prompt)
+    sku1_tb = gr.Textbox(label="Enter SKU for Product 1")
+    sku2_tb = gr.Textbox(label="Enter SKU for Product 2")
+    output_md = gr.Markdown(label="Comparison Result", value=default_response_filler)
+    submit_btn = gr.Button("Compare")
+
+    submit_btn.click(
+        fn=compare_cameras_gui,
+        inputs=[model_dd, verbosity_dd, reasoning_dd, prompt_tb, sku1_tb, sku2_tb],
+        outputs=output_md,
+    )
 
 def authenticate(username, password):
     return username == "bnh" and password == login_password
 
 # Launch the interface
 # Disable Gradio's experimental SSR to avoid svelte-i18n initial locale errors in server-side rendering
-iface.launch(auth=authenticate, share=True, ssr_mode=False)
+demo.launch(auth=authenticate, share=False, ssr_mode=False)