Update app.py

app.py

@@ -1,72 +1,74 @@
-import streamlit as st 
-from lida import Manager, TextGenerationConfig , llm  
-from dotenv import load_dotenv
-import os
-import openai
-from PIL import Image
-from io import BytesIO
-import base64
-
-load_dotenv()
-openai.api_key = os.getenv('OPENAI_API_KEY')
-
-def base64_to_image(base64_string):
-    # Decode the base64 string
-    byte_data = base64.b64decode(base64_string)
-    
-    # Use BytesIO to convert the byte data to image
-    return Image.open(BytesIO(byte_data))
-
-
-lida = Manager(text_gen = llm(provider="hf", model="uukuguy/speechless-llama2-hermes-orca-platypus-13b", device_map="auto"))
-textgen_config = TextGenerationConfig(n=1, temperature=0.5, use_cache=True)
-
-menu = st.sidebar.selectbox("Choose an Option", ["Summarize", "Question based Graph"])
-
-if menu == "Summarize":
-    st.subheader("Summarization of your Data")
-    file_uploader = st.file_uploader("Upload your CSV", type="csv")
-    if file_uploader is not None:
-        path_to_save = "filename.csv"
-        with open(path_to_save, "wb") as f:
-            f.write(file_uploader.getvalue())
-        summary = lida.summarize("filename.csv", summary_method="default", textgen_config=textgen_config)
-        st.write(summary)
-        goals = lida.goals(summary, n=2, textgen_config=textgen_config)
-        for goal in goals:
-            st.write(goal)
-        i = 0
-        library = "seaborn"
-        textgen_config = TextGenerationConfig(n=1, temperature=0.2, use_cache=True)
-        charts = lida.visualize(summary=summary, goal=goals[i], textgen_config=textgen_config, library=library)  
-        img_base64_string = charts[0].raster
-        img = base64_to_image(img_base64_string)
-        st.image(img)
-        
-
-
-        
-elif menu == "Question based Graph":
-    st.subheader("Query your Data to Generate Graph")
-    file_uploader = st.file_uploader("Upload your CSV", type="csv")
-    if file_uploader is not None:
-        path_to_save = "filename1.csv"
-        with open(path_to_save, "wb") as f:
-            f.write(file_uploader.getvalue())
-        text_area = st.text_area("Query your Data to Generate Graph", height=200)
-        if st.button("Generate Graph"):
-            if len(text_area) > 0:
-                st.info("Your Query: " + text_area)
-                lida = Manager(text_gen = llm("openai")) 
-                textgen_config = TextGenerationConfig(n=1, temperature=0.2, use_cache=True)
-                summary = lida.summarize("filename1.csv", summary_method="default", textgen_config=textgen_config)
-                user_query = text_area
-                charts = lida.visualize(summary=summary, goal=user_query, textgen_config=textgen_config)  
-                charts[0]
-                image_base64 = charts[0].raster
-                img = base64_to_image(image_base64)
-                st.image(img)
-            
-
-
-
+import streamlit as st 
+from lida import Manager, TextGenerationConfig , llm  
+from dotenv import load_dotenv
+import os
+import openai
+from PIL import Image
+from io import BytesIO
+import base64
+from accelerate import disk_offload
+
+load_dotenv()
+openai.api_key = os.getenv('OPENAI_API_KEY')
+
+def base64_to_image(base64_string):
+    # Decode the base64 string
+    byte_data = base64.b64decode(base64_string)
+    
+    # Use BytesIO to convert the byte data to image
+    return Image.open(BytesIO(byte_data))
+
+
+lida = Manager(text_gen = llm(provider="hf", model="uukuguy/speechless-llama2-hermes-orca-platypus-13b", device_map="auto"))
+disk_offload(model=lida, offload_dir="offload")
+textgen_config = TextGenerationConfig(n=1, temperature=0.5, use_cache=True)
+
+menu = st.sidebar.selectbox("Choose an Option", ["Summarize", "Question based Graph"])
+
+if menu == "Summarize":
+    st.subheader("Summarization of your Data")
+    file_uploader = st.file_uploader("Upload your CSV", type="csv")
+    if file_uploader is not None:
+        path_to_save = "filename.csv"
+        with open(path_to_save, "wb") as f:
+            f.write(file_uploader.getvalue())
+        summary = lida.summarize("filename.csv", summary_method="default", textgen_config=textgen_config)
+        st.write(summary)
+        goals = lida.goals(summary, n=2, textgen_config=textgen_config)
+        for goal in goals:
+            st.write(goal)
+        i = 0
+        library = "seaborn"
+        textgen_config = TextGenerationConfig(n=1, temperature=0.2, use_cache=True)
+        charts = lida.visualize(summary=summary, goal=goals[i], textgen_config=textgen_config, library=library)  
+        img_base64_string = charts[0].raster
+        img = base64_to_image(img_base64_string)
+        st.image(img)
+        
+
+
+        
+elif menu == "Question based Graph":
+    st.subheader("Query your Data to Generate Graph")
+    file_uploader = st.file_uploader("Upload your CSV", type="csv")
+    if file_uploader is not None:
+        path_to_save = "filename1.csv"
+        with open(path_to_save, "wb") as f:
+            f.write(file_uploader.getvalue())
+        text_area = st.text_area("Query your Data to Generate Graph", height=200)
+        if st.button("Generate Graph"):
+            if len(text_area) > 0:
+                st.info("Your Query: " + text_area)
+                lida = Manager(text_gen = llm("openai")) 
+                textgen_config = TextGenerationConfig(n=1, temperature=0.2, use_cache=True)
+                summary = lida.summarize("filename1.csv", summary_method="default", textgen_config=textgen_config)
+                user_query = text_area
+                charts = lida.visualize(summary=summary, goal=user_query, textgen_config=textgen_config)  
+                charts[0]
+                image_base64 = charts[0].raster
+                img = base64_to_image(image_base64)
+                st.image(img)
+            
+
+
+