Update app.py

app.py

@@ -11,7 +11,7 @@ import base64
 # Load environment variables
 load_dotenv()
 
-# Predefined model selection
+# Default models
 selected_llm_model_name = "mistralai/Mixtral-8x7B-Instruct-v0.1"
 selected_embed_model_name = "BAAI/bge-small-en-v1.5"
 vector_index = None
@@ -19,19 +19,9 @@ vector_index = None
 # Initialize the parser
 parser = LlamaParse(api_key=os.getenv("LLAMA_INDEX_API"), result_type='markdown')
 file_extractor = {
-    '.pdf': parser,
-    '.docx': parser,
-    '.doc': parser,
-    '.txt': parser,
-    '.csv': parser,
-    '.xlsx': parser,
-    '.pptx': parser,
-    '.html': parser,
-    '.jpg': parser,
-    '.jpeg': parser,
-    '.png': parser,
-    '.webp': parser,
-    '.svg': parser,
+    '.pdf': parser, '.docx': parser, '.doc': parser, '.txt': parser,
+    '.csv': parser, '.xlsx': parser, '.pptx': parser, '.html': parser,
+    '.jpg': parser, '.jpeg': parser, '.png': parser, '.webp': parser, '.svg': parser
 }
 
 # File processing function
@@ -43,7 +33,7 @@ def load_files(file_path: str):
         vector_index = VectorStoreIndex.from_documents(document, embed_model=embed_model)
         print(f"Parsing done for {file_path}")
         filename = os.path.basename(file_path)
-        return f"File upload status: {filename} is ready to write"
+        return f"File upload status: Ready to write ({filename})"
     except Exception as e:
         return f"An error occurred: {e}"
 
@@ -52,13 +42,8 @@ def respond(message, history):
     try:
         llm = HuggingFaceInferenceAPI(
             model_name=selected_llm_model_name,
-            contextWindow=8192,
-            maxTokens=1024,
-            temperature=0.3,
-            topP=0.9,
-            frequencyPenalty=0.5,
-            presencePenalty=0.5,
-            token=os.getenv("TOKEN")
+            contextWindow=8192, maxTokens=1024, temperature=0.3, topP=0.9,
+            frequencyPenalty=0.5, presencePenalty=0.5, token=os.getenv("TOKEN")
         )
         query_engine = vector_index.as_query_engine(llm=llm)
         bot_message = query_engine.query(message)
@@ -70,25 +55,23 @@ def respond(message, history):
         return f"An error occurred: {e}"
 
 # UI Setup
-with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Roboto Mono")]), css='footer {visibility: hidden}') as demo:
+with gr.Blocks(theme=gr.themes.Light(font=[gr.themes.GoogleFont("Roboto Mono")]), css='footer {visibility: hidden}') as demo:
     gr.Markdown("# DocBot📄🤖")
     with gr.Row():
         with gr.Column(scale=1):
-            file_input = gr.File(file_count="single", type='filepath', label="Upload document")
+            file_input = gr.File(file_count="single", type='filepath', label="Upload Document")
             btn = gr.Button("Submit", variant='primary')
             clear = gr.ClearButton()
-            output = gr.Text(label='File upload status')
+            output = gr.Text(label='File Upload Status')
         with gr.Column(scale=3):
             gr.ChatInterface(
                 fn=respond,
                 chatbot=gr.Chatbot(height=500),
-                theme="soft",
+                theme="light",
                 show_progress='full',
                 textbox=gr.Textbox(placeholder="Ask me questions on the uploaded document!", container=False)
             )
-    
-    # Set up Gradio interactions
-    btn.click(fn=load_files, inputs=file_input, outputs=output)
+    btn.click(fn=load_files, inputs=[file_input], outputs=output)
     clear.click(lambda: [None] * 2, outputs=[file_input, output])
 
 # Launch the demo