Update app.py

app.py

@@ -1,91 +1,85 @@
-import requests
-import os
-import json
-import gradio as gr
-from dotenv import load_dotenv
-
-load_dotenv()
-
-api_key = os.getenv('API_KEY')
-
-
-# Mapping function to class
-function_mapping = {
-    "Creation_dossier_kbis": 'POST',
-    "Redaction_non_juridique": 'DRAFT',
-    "Redaction_juridique": '0',
-    "Resume": '0',
-    "Salutations": 'DRAFT',
-    "Traduction": 'DRAFT',
-    "Information_utilisateur": 'GET',
-    "Information_dossier": 'GET',
-    "Information_personne_societe": 'GET',
-    "Autre_demande": '0'
-}
-
-# Load tools
-with open("tools-intent-detection.json", "r", encoding="utf-8") as file:
-    tools = json.load(file)
-
-# API configuration
-url = 'https://openai-dev-fra-001.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-10-21'
-headers = {
-    'api-key': api_key,
-    'Content-Type': 'application/json'
-}
-
-def get_model_response(user_prompt):
-    # messages list
-    messages = [
-        {
-            "role": "system",
-            "content": ""
-        },
-        {
-            "role": "user",
-            "content": user_prompt
-        }
-    ]
-    
-    # payload
-    data = {
-        "model": "gpt-4o",
-        "messages": messages,
-        "tools": tools,
-        "tool_choice": "required"
-    }
-    
-    # API call
-    response = requests.post(url, headers=headers, data=json.dumps(data))
-    
-    # Process the response
-    if response.status_code != 200:
-        return f"Error: {response.status_code} - {response.text}", "0"
-    else:
-        response_data = response.json()
-        if 'choices' in response_data:
-            reply = response_data["choices"][0]["message"]
-            
-            # Extract function name from tool_calls
-            if 'tool_calls' in reply and len(reply['tool_calls']) > 0:
-                function_name = reply['tool_calls'][0]['function']['name']
-                # Get the corresponding class from the mapping
-                category = function_mapping.get(function_name, "0")  # 0 is default if function name not found
-                return function_name, category
-            else:
-                return "No function called", "0"
-        else:
-            return "Unexpected response format.", "0"
-
-# Gradio app
-iface = gr.Interface(
-    title="Intent Detection Playground",
-    fn=get_model_response,
-    inputs=gr.Textbox(label="User Prompt"),
-    outputs=[
-        gr.Textbox(label="Intention"),
-        gr.Textbox(label="Category")
-    ]
-)
-
+import requests
+import os
+import json
+import gradio as gr
+
+# Mapping function to class
+function_mapping = {
+    "Creation_dossier_kbis": 'POST',
+    "Redaction_non_juridique": 'DRAFT',
+    "Redaction_juridique": '0',
+    "Resume": '0',
+    "Salutations": 'DRAFT',
+    "Traduction": 'DRAFT',
+    "Information_utilisateur": 'GET',
+    "Information_dossier": 'GET',
+    "Information_personne_societe": 'GET',
+    "Autre_demande": '0'
+}
+
+# Load tools
+with open("tools-intent-detection.json", "r", encoding="utf-8") as file:
+    tools = json.load(file)
+
+# API configuration
+url = 'https://openai-dev-fra-001.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-10-21'
+headers = {
+    'api-key': API_KEY,
+    'Content-Type': 'application/json'
+}
+
+def get_model_response(user_prompt):
+    # messages list
+    messages = [
+        {
+            "role": "system",
+            "content": ""
+        },
+        {
+            "role": "user",
+            "content": user_prompt
+        }
+    ]
+    
+    # payload
+    data = {
+        "model": "gpt-4o",
+        "messages": messages,
+        "tools": tools,
+        "tool_choice": "required"
+    }
+    
+    # API call
+    response = requests.post(url, headers=headers, data=json.dumps(data))
+    
+    # Process the response
+    if response.status_code != 200:
+        return f"Error: {response.status_code} - {response.text}", "0"
+    else:
+        response_data = response.json()
+        if 'choices' in response_data:
+            reply = response_data["choices"][0]["message"]
+            
+            # Extract function name from tool_calls
+            if 'tool_calls' in reply and len(reply['tool_calls']) > 0:
+                function_name = reply['tool_calls'][0]['function']['name']
+                # Get the corresponding class from the mapping
+                category = function_mapping.get(function_name, "0")  # 0 is default if function name not found
+                return function_name, category
+            else:
+                return "No function called", "0"
+        else:
+            return "Unexpected response format.", "0"
+
+# Gradio app
+iface = gr.Interface(
+    title="Intent Detection Playground",
+    fn=get_model_response,
+    inputs=gr.Textbox(label="User Prompt"),
+    outputs=[
+        gr.Textbox(label="Intention"),
+        gr.Textbox(label="Category")
+    ]
+)
+
 iface.launch()