update

app.py

@@ -1,6 +1,8 @@
+import streamlit as st
+import pandas as pd
 import json
 import http.client
-import streamlit as st
+from io import StringIO
 from openai import AzureOpenAI
 
 class ContentFormatter:
@@ -14,18 +16,20 @@ class ContentFormatter:
         return json.dumps(data)
 
 class AzureAgent:
-    def __init__(self, api_key, azure_uri):
+    def __init__(self, api_key, azure_uri, deployment_name, api_version):
         self.azure_uri = azure_uri
         self.headers = {
             'Authorization': f"Bearer {api_key}",
             'Content-Type': 'application/json'
         }
+        self.deployment_name = deployment_name
+        self.api_version = api_version
         self.chat_formatter = ContentFormatter
 
     def invoke(self, text, **kwargs):
         body = self.chat_formatter.chat_completions(text, {**kwargs})
         conn = http.client.HTTPSConnection(self.azure_uri)
-        conn.request("POST", '/v1/chat/completions', body=body, headers=self.headers)
+        conn.request("POST", f'/v{self.api_version}/chat/completions', body=body, headers=self.headers)
         response = conn.getresponse()
         data = response.read()
         conn.close()
@@ -35,13 +39,13 @@ class AzureAgent:
         return content
 
 class GPTAgent:
-    def __init__(self, api_key, azure_endpoint):
+    def __init__(self, api_key, azure_endpoint, deployment_name, api_version='2022-11-01'):
         self.client = AzureOpenAI(
             api_key=api_key,
-            api_version='2022-11-01',  # Defaulting to a specific API version
+            api_version=api_version,
             azure_endpoint=azure_endpoint
         )
-        self.deployment_name = 'your-model-deployment-name'  # Update this based on actual deployment name
+        self.deployment_name = deployment_name
 
     def invoke(self, text, **kwargs):
         response = self.client.chat.completions.create(
@@ -60,6 +64,8 @@ st.title('Model Invocation with Dataset Processing')
 model_type = st.radio("Select the type of agent", ('AzureAgent', 'GPTAgent'))
 api_key = st.text_input("API Key", type="password")
 endpoint_url = st.text_input("Endpoint URL")
+deployment_name = st.text_input("Model Name")
+api_version = st.text_input("API Version")  # Default API version
 
 # File upload and data display
 uploaded_file = st.file_uploader("Choose a file")
@@ -72,9 +78,9 @@ if uploaded_file is not None:
     # Process data button
     if st.button('Process Data'):
         if model_type == 'AzureAgent':
-            agent = AzureAgent(api_key, endpoint_url)
+            agent = AzureAgent(api_key, endpoint_url, deployment_name, api_version)
         else:
-            agent = GPTAgent(api_key, endpoint_url)
+            agent = GPTAgent(api_key, endpoint_url, deployment_name, api_version)
 
         # Example processing step (adapt as needed)
         df['Response'] = df['Input Column'].apply(lambda x: agent.invoke(x, temperature=0, max_tokens=150))
@@ -87,4 +93,4 @@ if uploaded_file is not None:
             data=df.to_csv().encode('utf-8'),
             file_name='processed_data.csv',
             mime='text/csv',
-        )
+        )

requirements.txt

@@ -1 +1,2 @@
-openai
+openai
+pandas