Update app.py

app.py

@@ -5,8 +5,6 @@ import spaces
 from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 import os
 from threading import Thread
-from fastapi import FastAPI
-import uvicorn
 
 import pymupdf
 import docx
@@ -35,11 +33,15 @@ h1 {
 }
 """
 
+
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
 
+
+
 def extract_text(path):
     return open(path, 'r').read()
 
+
 def extract_pdf(path):
     doc = pymupdf.open(path)
     text = ""
@@ -47,6 +49,7 @@ def extract_pdf(path):
         text += page.get_text()
     return text
 
+
 def extract_docx(path):
     doc = docx.Document(path)
     data = []
@@ -55,6 +58,7 @@ def extract_docx(path):
     content = '\n\n'.join(data)
     return content
 
+
 def extract_pptx(path):
     prs = Presentation(path)
     text = ""
@@ -64,6 +68,7 @@ def extract_pptx(path):
                 text += shape.text + "\n"
     return text
 
+
 def mode_load(path):
     choice = ""
     file_type = path.split(".")[-1]
@@ -80,15 +85,20 @@ def mode_load(path):
         choice = "doc"
         print(content[:100])
         return choice, content[:5000]
+
+
     elif file_type in ["png", "jpg", "jpeg", "bmp", "tiff", "webp"]:
         content = Image.open(path).convert('RGB')
         choice = "image"
         return choice, content
+
     else:
         raise gr.Error("Oops, unsupported files.")
 
+
 @spaces.GPU()
 def stream_chat(message, history: list, temperature: float, max_length: int, top_p: float, top_k: int, penalty: float):
+    
     model = AutoModelForCausalLM.from_pretrained(
         MODEL_ID,
         torch_dtype=torch.bfloat16,
@@ -126,6 +136,7 @@ def stream_chat(message, history: list, temperature: float, max_length: int, top
                 choice = ""
                 conversation.append({"role": "user", "image": "", "content": message['text']})
 
+
             if choice == "image":
                 conversation.append({"role": "user", "image": contents, "content": message['text']})
             elif choice == "doc":
@@ -157,11 +168,18 @@ def stream_chat(message, history: list, temperature: float, max_length: int, top
             buffer += new_text
             yield buffer
 
-chatbot = gr.Chatbot()
+
+chatbot = gr.Chatbot(
+    #rtl=True,
+)
 chat_input = gr.MultimodalTextbox(
     interactive=True,
     placeholder="Enter message or upload a file ...",
     show_label=False,
+    #rtl=True,
+
+
+
 )
 
 EXAMPLES = [
@@ -171,73 +189,63 @@ EXAMPLES = [
     [{"text": "Quiero armar un JSON, solo el JSON sin texto, que contenga los datos de la primera mitad de la tabla de la imagen (las primeras 10 jurisdicciones 901-910). Ten en cuenta que los valores numéricos son decimales de cuatro dígitos. La tabla contiene las siguientes columnas: Codigo, Nombre, Fecha Inicio, Fecha Cese, Coeficiente Ingresos, Coeficiente Gastos y Coeficiente Unificado. La tabla puede contener valores vacíos, en ese caso dejarlos como null. Cada fila de la tabla representa una jurisdicción con sus respectivos valores.", }]
 ]
 
-app = FastAPI()
-
-def test():
-    return "Función test llamada con éxito"
-
-@app.get("/test")
-def call_test():
-    return {"message": test()}
-
-def run_gradio():
-    with gr.Blocks(css=CSS, theme="soft", fill_height=True) as demo:
-        gr.HTML(TITLE)
-        gr.HTML(DESCRIPTION)
-        gr.ChatInterface(
-            fn=stream_chat,
-            multimodal=True,
-            textbox=chat_input,
-            chatbot=chatbot,
-            fill_height=True,
-            additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
-            additional_inputs=[
-                gr.Slider(
-                    minimum=0,
-                    maximum=1,
-                    step=0.1,
-                    value=0.8,
-                    label="Temperature",
-                    render=False,
-                ),
-                gr.Slider(
-                    minimum=1024,
-                    maximum=8192,
-                    step=1,
-                    value=4096,
-                    label="Max Length",
-                    render=False,
-                ),
-                gr.Slider(
-                    minimum=0.0,
-                    maximum=1.0,
-                    step=0.1,
-                    value=1.0,
-                    label="top_p",
-                    render=False,
-                ),
-                gr.Slider(
-                    minimum=1,
-                    maximum=20,
-                    step=1,
-                    value=10,
-                    label="top_k",
-                    render=False,
-                ),
-                gr.Slider(
-                    minimum=0.0,
-                    maximum=2.0,
-                    step=0.1,
-                    value=1.0,
-                    label="Repetition penalty",
-                    render=False,
-                ),
-            ],
-        ),
-        gr.Examples(EXAMPLES, [chat_input])
-    demo.queue(api_open=False).launch(show_api=False, share=False)
+with gr.Blocks(css=CSS, theme="soft", fill_height=True) as demo:
+    gr.HTML(TITLE)
+    gr.HTML(DESCRIPTION)
+    gr.ChatInterface(
+        fn=stream_chat,
+        multimodal=True,
+
+
+        textbox=chat_input,
+        chatbot=chatbot,
+        fill_height=True,
+        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
+        additional_inputs=[
+            gr.Slider(
+                minimum=0,
+                maximum=1,
+                step=0.1,
+                value=0.8,
+                label="Temperature",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=1024,
+                maximum=8192,
+                step=1,
+                value=4096,
+                label="Max Length",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=0.0,
+                maximum=1.0,
+                step=0.1,
+                value=1.0,
+                label="top_p",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=1,
+                maximum=20,
+                step=1,
+                value=10,
+                label="top_k",
+                render=False,
+            ),
+            gr.Slider(
+                minimum=0.0,
+                maximum=2.0,
+                step=0.1,
+                value=1.0,
+                label="Repetition penalty",
+                render=False,
+            ),
+        ],
+    ),
+    gr.Examples(EXAMPLES, [chat_input])
 
 if __name__ == "__main__":
-    gradio_thread = Thread(target=run_gradio)
-    gradio_thread.start()
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    
+    demo.queue(api_open=False).launch(show_api=False, share=False, )#server_name="0.0.0.0", )