ai: Implementing gradio multimodal textbox.
Browse files
app.py
CHANGED
@@ -29,6 +29,9 @@ import fitz
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import io
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from pathlib import Path
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from PIL import Image
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LINUX_SERVER_HOSTS = [host for host in json.loads(os.getenv("LINUX_SERVER_HOST", "[]")) if host]
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LINUX_SERVER_PROVIDER_KEYS = [key for key in json.loads(os.getenv("LINUX_SERVER_PROVIDER_KEY", "[]")) if key]
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@@ -45,196 +48,114 @@ META_TAGS = os.getenv("META_TAGS")
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ALLOWED_EXTENSIONS = json.loads(os.getenv("ALLOWED_EXTENSIONS"))
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stop_event = threading.Event()
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session = requests.Session()
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def get_model_key(display_name):
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return next((k for k, v in MODEL_MAPPING.items() if v == display_name), MODEL_CHOICES[0])
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def
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try:
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with open(file_path, "r", encoding="utf-8") as file:
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return file.read()
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except:
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return ""
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with pdfplumber.open(file_path) as pdf:
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for page in pdf.pages:
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text
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doc = docx.Document(file_path)
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return text
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except:
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return ""
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elif ext in [".xls", ".xlsx"]:
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try:
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df = pd.read_excel(file_path)
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elif ext in [".ppt", ".pptx"]:
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try:
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prs = pptx.Presentation(file_path)
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text = []
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for slide in prs.slides:
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for shape in slide.shapes:
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if hasattr(shape, "text"):
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text.append(pytesseract.image_to_string(img))
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except:
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return []
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return text
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def extract_text_from_doc_images(doc_path):
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text = []
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try:
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doc = docx.Document(doc_path)
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for rel in doc.part.rels:
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if "image" in doc.part.rels[rel].target_ref:
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img_data = doc.part.rels[rel].target_part.blob
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img = Image.open(io.BytesIO(img_data))
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text.append(pytesseract.image_to_string(img))
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except:
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return []
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return "\n".join(text)
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def simulate_streaming_response(text):
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for line in text.splitlines():
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if stop_event.is_set():
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return
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yield line + "\n"
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time.sleep(0.05)
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def chat_with_model(history, user_input, selected_model_display):
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if stop_event.is_set():
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yield RESPONSES["RESPONSE_1"]
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return
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if not LINUX_SERVER_PROVIDER_KEYS or not LINUX_SERVER_HOSTS:
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yield RESPONSES["RESPONSE_3"]
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return
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selected_model = get_model_key(selected_model_display)
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model_config = MODEL_CONFIG.get(selected_model, DEFAULT_CONFIG)
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messages = [{"role": "user", "content": user} for user, _ in history]
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messages += [{"role": "assistant", "content": assistant} for _, assistant in history if assistant]
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messages.append({"role": "user", "content": user_input})
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data = {"model": selected_model, "messages": messages, **model_config}
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random.shuffle(LINUX_SERVER_PROVIDER_KEYS)
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random.shuffle(LINUX_SERVER_HOSTS)
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for api_key in LINUX_SERVER_PROVIDER_KEYS[:2]:
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for host in LINUX_SERVER_HOSTS[:2]:
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if stop_event.is_set():
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yield RESPONSES["RESPONSE_1"]
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return
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try:
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response = session.post(host, json=data, headers={"Authorization": f"Bearer {api_key}"})
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if stop_event.is_set():
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yield RESPONSES["RESPONSE_1"]
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return
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if response.status_code < 400:
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ai_text = response.json().get("choices", [{}])[0].get("message", {}).get("content", RESPONSES["RESPONSE_2"])
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yield from simulate_streaming_response(ai_text)
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return
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except requests.exceptions.RequestException:
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continue
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yield RESPONSES["RESPONSE_3"]
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def respond(
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ai_response = ""
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for chunk in chat_with_model(history, combined_input, selected_model_display):
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if stop_event.is_set():
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history[-1][1] = RESPONSES["RESPONSE_1"]
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yield history, gr.update(value=""), gr.update(visible=True), gr.update(visible=False)
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return
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ai_response += chunk
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history[-1][1] = ai_response
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yield history, gr.update(value=""), gr.update(visible=True), gr.update(visible=False)
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def stop_response():
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stop_event.set()
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session.close()
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def change_model(new_model_display):
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return [], new_model_display
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def file_type_validation(file):
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if file:
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ext = Path(file.name).suffix.lower()
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return ext in ALLOWED_EXTENSIONS
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return False
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def check_send_button_enabled(msg, file):
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is_file_valid = file_type_validation(file)
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return gr.update(interactive=bool(msg.strip()) or bool(file))
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with gr.Blocks(fill_height=True, fill_width=True, title=AI_TYPES["AI_TYPE_4"], head=META_TAGS) as demo:
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user_history = gr.State([])
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selected_model = gr.State(MODEL_CHOICES[0])
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msg = gr.Textbox(label=RESPONSES["RESPONSE_4"], show_label=False, placeholder=RESPONSES["RESPONSE_5"], interactive=True)
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send_btn = gr.Button(RESPONSES["RESPONSE_6"], visible=True, interactive=False)
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stop_btn = gr.Button(RESPONSES["RESPONSE_7"], variant=RESPONSES["RESPONSE_9"], visible=False)
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with gr.Accordion(AI_TYPES["AI_TYPE_6"], open=False):
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file_upload = gr.File(label=AI_TYPES["AI_TYPE_5"], file_count="single", type="filepath", file_types=ALLOWED_EXTENSIONS)
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model_dropdown.change(fn=change_model, inputs=[model_dropdown], outputs=[user_history, selected_model])
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msg.change(fn=check_send_button_enabled, inputs=[msg, file_upload], outputs=[send_btn])
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stop_btn.click(fn=stop_response, outputs=[send_btn, stop_btn])
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file_upload.change(fn=check_send_button_enabled, inputs=[msg, file_upload], outputs=[send_btn])
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demo.launch(show_api=False, max_file_size="1mb")
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import io
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from pathlib import Path
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from PIL import Image
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from pptx import Presentation
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os.system("apt-get update -q -y && apt-get install -q -y tesseract-ocr tesseract-ocr-eng tesseract-ocr-ind libleptonica-dev libtesseract-dev")
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LINUX_SERVER_HOSTS = [host for host in json.loads(os.getenv("LINUX_SERVER_HOST", "[]")) if host]
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LINUX_SERVER_PROVIDER_KEYS = [key for key in json.loads(os.getenv("LINUX_SERVER_PROVIDER_KEY", "[]")) if key]
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ALLOWED_EXTENSIONS = json.loads(os.getenv("ALLOWED_EXTENSIONS"))
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session = requests.Session()
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def get_model_key(display_name):
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return next((k for k, v in MODEL_MAPPING.items() if v == display_name), MODEL_CHOICES[0])
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def simulate_streaming_response(text):
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for line in text.splitlines():
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yield line + "\n"
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time.sleep(0.05)
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def extract_file_content(file_path):
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ext = Path(file_path).suffix.lower()
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content = ""
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try:
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if ext == ".pdf":
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with pdfplumber.open(file_path) as pdf:
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for page in pdf.pages:
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text = page.extract_text()
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if text:
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content += text + "\n"
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tables = page.extract_tables()
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if tables:
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for table in tables:
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table_str = "\n".join([", ".join(row) for row in table if row])
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content += "\n" + table_str + "\n"
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elif ext in [".doc", ".docx"]:
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doc = docx.Document(file_path)
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for para in doc.paragraphs:
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content += para.text + "\n"
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elif ext in [".xlsx", ".xls"]:
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df = pd.read_excel(file_path)
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content += df.to_csv(index=False)
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elif ext in [".ppt", ".pptx"]:
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prs = Presentation(file_path)
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for slide in prs.slides:
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for shape in slide.shapes:
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if hasattr(shape, "text") and shape.text:
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content += shape.text + "\n"
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elif ext in [".png", ".jpg", ".jpeg", ".tiff", ".bmp", ".gif", ".webp"]:
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try:
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pytesseract.pytesseract.tesseract_cmd = "/usr/bin/tesseract"
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image = Image.open(file_path)
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text = pytesseract.image_to_string(image)
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content += text + "\n"
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except Exception as e:
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content += f"{e}\n"
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else:
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content = Path(file_path).read_text(encoding="utf-8")
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except Exception as e:
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content = f"{file_path}: {e}"
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return content.strip()
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def chat_with_model(history, user_input, selected_model_display):
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if not LINUX_SERVER_PROVIDER_KEYS or not LINUX_SERVER_HOSTS:
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yield RESPONSES["RESPONSE_3"]
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return
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selected_model = get_model_key(selected_model_display)
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model_config = MODEL_CONFIG.get(selected_model, DEFAULT_CONFIG)
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messages = [{"role": "user", "content": user} for user, _ in history]
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messages += [{"role": "assistant", "content": assistant} for _, assistant in history if assistant]
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messages.append({"role": "user", "content": user_input})
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data = {"model": selected_model, "messages": messages, **model_config}
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random.shuffle(LINUX_SERVER_PROVIDER_KEYS)
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random.shuffle(LINUX_SERVER_HOSTS)
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for api_key in LINUX_SERVER_PROVIDER_KEYS[:2]:
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for host in LINUX_SERVER_HOSTS[:2]:
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try:
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response = session.post(host, json=data, headers={"Authorization": f"Bearer {api_key}"})
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if response.status_code < 400:
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ai_text = response.json().get("choices", [{}])[0].get("message", {}).get("content", RESPONSES["RESPONSE_2"])
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yield from simulate_streaming_response(ai_text)
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return
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except requests.exceptions.RequestException:
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continue
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yield RESPONSES["RESPONSE_3"]
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def respond(multi_input, history, selected_model_display):
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message = {"text": multi_input.get("text", "").strip(), "files": multi_input.get("files", [])}
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if not message["text"] and not message["files"]:
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return history, gr.MultimodalTextbox(value=None, interactive=True)
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combined_input = ""
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for file_item in message["files"]:
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if isinstance(file_item, dict) and "name" in file_item:
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file_path = file_item["name"]
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else:
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file_path = file_item
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file_content = extract_file_content(file_path)
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combined_input += f"{Path(file_path).name}\n\n{file_content}\n\n"
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if message["text"]:
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combined_input += message["text"]
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history.append([combined_input, ""])
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ai_response = ""
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for chunk in chat_with_model(history, combined_input, selected_model_display):
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ai_response += chunk
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history[-1][1] = ai_response
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return history, gr.MultimodalTextbox(value=None, interactive=True)
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def change_model(new_model_display):
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return [], new_model_display
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with gr.Blocks(fill_height=True, fill_width=True, title=AI_TYPES["AI_TYPE_4"], head=META_TAGS) as demo:
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user_history = gr.State([])
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selected_model = gr.State(MODEL_CHOICES[0])
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chatbot = gr.Chatbot(label=AI_TYPES["AI_TYPE_1"], show_copy_button=True, scale=1, elem_id=AI_TYPES["AI_TYPE_2"])
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model_dropdown = gr.Dropdown(show_label=False, choices=MODEL_CHOICES, value=MODEL_CHOICES[0])
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msg = gr.MultimodalTextbox(show_label=False, placeholder=RESPONSES["RESPONSE_5"], scale=0, interactive=True, file_count="single", file_types=ALLOWED_EXTENSIONS)
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model_dropdown.change(fn=change_model, inputs=[model_dropdown], outputs=[user_history, selected_model])
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msg.submit(fn=respond, inputs=[msg, user_history, selected_model], outputs=[chatbot, msg])
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demo.launch(show_api=False, max_file_size="1mb")
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