update

app.py

@@ -55,18 +55,36 @@ class GPTAgent:
         return response.choices[0].message.content
 
 # Streamlit app interface
-st.title('Model Invocation with User Credentials')
+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")
 
-input_text = st.text_area('Enter your text')
-if st.button('Invoke Model'):
-    if model_type == 'AzureAgent':
-        agent = AzureAgent(api_key, endpoint_url)
-    else:
-        agent = GPTAgent(api_key, endpoint_url)
+# File upload and data display
+uploaded_file = st.file_uploader("Choose a file")
+if uploaded_file is not None:
+    # Read data
+    data = StringIO(uploaded_file.getvalue().decode("utf-8"))
+    df = pd.read_csv(data)
+    st.write('Uploaded Data:', df.head())
 
-    output = agent.invoke(input_text, temperature=0, max_tokens=5)
-    st.write('Response:', output)
+    # Process data button
+    if st.button('Process Data'):
+        if model_type == 'AzureAgent':
+            agent = AzureAgent(api_key, endpoint_url)
+        else:
+            agent = GPTAgent(api_key, endpoint_url)
+
+        # Example processing step (adapt as needed)
+        df['Response'] = df['Input Column'].apply(lambda x: agent.invoke(x, temperature=0, max_tokens=150))
+
+        st.write('Processed Data:', df.head())
+
+        # Generate download link
+        st.download_button(
+            label="Download processed data",
+            data=df.to_csv().encode('utf-8'),
+            file_name='processed_data.csv',
+            mime='text/csv',
+        )