Update app.py

app.py

@@ -1,27 +1,156 @@
 import gradio as gr
+import os
+import re
+import time
+import torch
+import tempfile
+from datetime import datetime
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from gtts import gTTS
+from PIL import Image
+from diffusers import StableDiffusionPipeline
+from PyPDF2 import PdfReader
+import speech_recognition as sr
 
-def chat(message, history):
+# ================== تنظیمات اولیه ==================
+
+os.makedirs("conversations", exist_ok=True)  # پوشه آرشیو گفتگوها
+
+en_model_name = "HuggingFaceH4/zephyr-7b-beta"
+fa_model_name = "HooshvareLab/gpt2-fa"
+
+# بارگذاری مدل‌ها
+en_tokenizer = AutoTokenizer.from_pretrained(en_model_name)
+en_model = AutoModelForCausalLM.from_pretrained(en_model_name, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
+en_model.eval()
+
+fa_tokenizer = AutoTokenizer.from_pretrained(fa_model_name)
+fa_model = AutoModelForCausalLM.from_pretrained(fa_model_name)
+fa_model.eval()
+
+# بارگذاری مدل تولید تصویر
+image_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
+image_pipe = image_pipe.to("cuda" if torch.cuda.is_available() else "cpu")
+
+# ================== توابع کمکی ==================
+
+def detect_language(text):
+    return bool(re.search(r'[\u0600-\u06FF]', text))
+
+def save_conversation(history, file_path):
+    with open(file_path, "w", encoding="utf-8") as f:
+        for user, bot in history:
+            f.write(f"User: {user}\nAssistant: {bot}\n\n")
+
+def load_conversation(file_path):
+    history = []
+    with open(file_path, encoding="utf-8") as f:
+        content = f.read().strip().split("\n\n")
+        for turn in content:
+            if turn.strip():
+                parts = turn.split("\n")
+                if len(parts) == 2:
+                    user = parts[0].replace("User: ", "")
+                    bot = parts[1].replace("Assistant: ", "")
+                    history.append((user, bot))
+    return history
+
+def list_conversations():
+    files = os.listdir("conversations")
+    files.sort(reverse=True)
+    return [f for f in files if f.endswith(".txt")]
+
+# ================== تابع اصلی چت ==================
+
+def chat_with_bot(message, history, selected_file):
+    is_farsi = detect_language(message)
     history = history or []
-    history.append(("You", message))
-    
-    # پاسخ ساده برای تست
-    if "سلام" in message:
-        reply = "سلام! چطور می‌تونم کمکت کنم؟"
-    elif "hello" in message.lower():
-        reply = "Hi there! How can I help you?"
+
+    full_prompt = ""
+    for user, bot in history:
+        full_prompt += f"User: {user}\nAssistant: {bot}\n"
+    full_prompt += f"User: {message}\nAssistant:"
+
+    if is_farsi:
+        inputs = fa_tokenizer(full_prompt, return_tensors="pt", truncation=True)
+        outputs = fa_model.generate(**inputs, max_new_tokens=100, pad_token_id=fa_tokenizer.eos_token_id)
+        response = fa_tokenizer.decode(outputs[0], skip_special_tokens=True)
     else:
-        reply = "متوجه نشدم. لطفاً دوباره بپرس."
+        inputs = en_tokenizer(full_prompt, return_tensors="pt", truncation=True)
+        outputs = en_model.generate(**inputs, max_new_tokens=150, pad_token_id=en_tokenizer.eos_token_id)
+        response = en_tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-    history.append(("Bot", reply))
-    return "", history
+    response = response.split("Assistant:")[-1].strip()
+    history.append((message, response))
+
+    timestamp = selected_file if selected_file else datetime.now().strftime("%Y-%m-%d_%H-%M") + ".txt"
+    save_conversation(history, os.path.join("conversations", timestamp))
+
+    # تولید صدا
+    tts = gTTS(text=response, lang='fa' if is_farsi else 'en')
+    audio_path = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False).name
+    tts.save(audio_path)
+
+    return history, history, audio_path, list_conversations(), timestamp
+
+# ================== پردازش فایل‌ها ==================
+
+def handle_pdf(file):
+    reader = PdfReader(file.name)
+    text = "\n".join(page.extract_text() for page in reader.pages if page.extract_text())
+    summary = text[:1000] + ("..." if len(text) > 1000 else "")
+    return summary
+
+def handle_image(file):
+    import pytesseract
+    image = Image.open(file.name)
+    return pytesseract.image_to_string(image, lang='fas+eng')
+
+def handle_audio(file):
+    recognizer = sr.Recognizer()
+    with sr.AudioFile(file.name) as source:
+        audio_data = recognizer.record(source)
+        try:
+            text = recognizer.recognize_google(audio_data, language="fa-IR")
+        except:
+            text = "صدا قابل شناسایی نبود."
+    return text
+
+def generate_image(prompt):
+    image = image_pipe(prompt).images[0]
+    return image
+
+# ================== رابط گرافیکی ==================
 
 with gr.Blocks() as demo:
-    gr.Markdown("# چت‌بات چندزبانه ساده")
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox(placeholder="پیام خود را وارد کنید...")
-    clear = gr.Button("پاک کردن گفتگو")
+    gr.Markdown("# AIChat-FaEnPro | دستیار هوشمند فارسی-انگلیسی")
+
+    with gr.Row():
+        with gr.Column():
+            chatbot = gr.Chatbot()
+            msg = gr.Textbox(label="پیام شما")
+            audio_in = gr.Audio(source="microphone", type="filepath", label="ورودی صوتی")
+            submit = gr.Button("ارسال")
+            clear = gr.Button("پاک کردن گفتگو")
+            archive_dropdown = gr.Dropdown(label="گفتگوهای ذخیره‌شده", choices=list_conversations(), interactive=True)
+
+        with gr.Column():
+            file_input = gr.File(label="آپلود فایل (PDF/تصویر/صدا)")
+            file_output = gr.Textbox(label="نتیجه پردازش فایل")
+            image_prompt = gr.Textbox(label="دستور تولید تصویر")
+            image_out = gr.Image(label="تصویر تولید شده")
+            audio_out = gr.Audio(label="صدای پاسخ")
+
+    state = gr.State([])
+    file_state = gr.State("")
+
+    submit.click(chat_with_bot, [msg, state, file_state], [chatbot, state, audio_out, archive_dropdown, file_state])
+    clear.click(lambda: ([], [], None, list_conversations(), ""), None, [chatbot, state, audio_out, archive_dropdown, file_state])
+    archive_dropdown.change(lambda f: (load_conversation(os.path.join("conversations", f)), f), [archive_dropdown], [state, file_state])
+    audio_in.change(lambda f, h, s: chat_with_bot(handle_audio(f), h, s), [audio_in, state, file_state], [chatbot, state, audio_out, archive_dropdown, file_state])
+    file_input.change(lambda f: handle_pdf(f) if f.name.endswith(".pdf") else handle_image(f) if f.type.startswith("image") else handle_audio(f), file_input, file_output)
+    image_prompt.submit(generate_image, image_prompt, image_out)
 
-    msg.submit(chat, [msg, chatbot], [msg, chatbot])
-    clear.click(lambda: None, None, chatbot, queue=False)
+    gr.Markdown("طراحی شده توسط شما، با قدرت ChatGPT")
 
 demo.launch()