Update app.py

app.py

@@ -1,70 +1,66 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import yaml
+import sqlite3
+import difflib
+import re
 
+# ✅ Emotion detection (সাধারণ রুল-বেসড)
+def detect_emotion(text):
+    text = text.lower()
+    if any(word in text for word in ["ভালো", "সুন্দর", "ধন্যবাদ", "আনন্দ"]):
+        return "আনন্দ"
+    elif any(word in text for word in ["দুঃখ", "কষ্ট", "হারিয়েছি", "চিন্তা"]):
+        return "দুঃখ"
+    elif any(word in text for word in ["রাগ", "বিরক্ত", "না", "খারাপ"]):
+        return "রাগ"
+    elif any(word in text for word in ["কি", "কেন", "কিভাবে", "বলো"]):
+        return "কৌতূহল"
+    else:
+        return "নিরপেক্ষ"
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = git commit -am 'Update space' && git push, model="openai/gpt-oss-20b")
+# ✅ YAML থেকে QA লোড করুন
+def load_qa_yaml(path="bonolota_ai_dataset/qa.yaml"):
+    with open(path, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
 
-    messages = [{"role": "system", "content": system_message}]
+qa_data = load_qa_yaml()
 
-    messages.extend(history)
+# ✅ প্রশ্নের fuzzy match খুঁজে বের করুন
+def fuzzy_match(query, qa_data, threshold=0.6):
+    questions = [item["question"] for item in qa_data]
+    matches = difflib.get_close_matches(query, questions, n=1, cutoff=threshold)
+    if matches:
+        for item in qa_data:
+            if item["question"] == matches[0]:
+                return item
+    return None
 
-    messages.append({"role": "user", "content": message})
+# ✅ চ্যাট রেসপন্স ফাংশন
+def respond(message, history):
+    emotion = detect_emotion(message)
+    matched = fuzzy_match(message, qa_data)
 
-    response = ""
+    if matched:
+        response = f"📖 উত্তর: {matched['answer']}\n😌 আবেগ: {matched['emotion']}"
+        if "voice_path" in matched:
+            response += f"\n🔊 ভয়েস: {matched['voice_path']}"
+        if "photo_path" in matched:
+            response += f"\n🖼️ ছবি: {matched['photo_path']}"
+    else:
+        response = f"😔 দুঃখিত, আমি এই প্রশ্নের উত্তর খুঁজে পেলাম না।\n🧠 আবেগ শনাক্ত: {emotion}"
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+    history.append((message, response))
+    return "", history
 
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# ✅ Gradio UI
 chatbot = gr.ChatInterface(
     respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+    title="🌿 Bonolota_AI (লোকাল বাংলা চ্যাটবট)",
+    theme="soft",
+    examples=["তোমার নাম কী?", "বাংলাদেশের রাজধানী কী?", "আমি খুব দুঃখিত", "তুমি কিভাবে কাজ করো?"],
+    cache_examples=False,
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
-
-
+# ✅ Launch
 if __name__ == "__main__":
-    demo.launch()
+    chatbot.launch()