Create app.py

app.py

@@ -0,0 +1,118 @@
+# app_gradio.py
+import gradio as gr
+from deep_translator import GoogleTranslator
+from langdetect import detect
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+import re # Import regex for post-processing
+
+MODEL_DIR = "./fine_tuned_model"
+
+def load_model():
+    tokenizer = GPT2Tokenizer.from_pretrained(MODEL_DIR, local_files_only=True)
+    model = GPT2LMHeadModel.from_pretrained(MODEL_DIR, local_files_only=True)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+        model.config.pad_token_id = tokenizer.pad_token_id
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device).eval()
+    return tokenizer, model, device
+
+tokenizer, model, device = load_model()
+
+def to_en(text):
+    try:
+        lang = detect(text)
+    except Exception:
+        lang = "en"
+    if lang == "en":
+        return text, "en"
+    translated_text = GoogleTranslator(source=lang, target="en").translate(text)
+    # Handle potential None return from translator
+    return translated_text if translated_text is not None else text, lang
+
+def from_en(text, tgt):
+    if tgt == "en":
+        return text
+    translated_text = GoogleTranslator(source="en", target=tgt).translate(text)
+    # Handle potential None return from translator
+    return translated_text if translated_text is not None else text
+
+def generate(prompt, max_new_tokens=120, temperature=0.8):
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    with torch.no_grad():
+        out = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            top_k=50,
+            top_p=0.95,
+            temperature=temperature,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+        )
+    return tokenizer.decode(out[0], skip_special_tokens=True)
+
+def post_process_generated_text(text, prompt):
+    # Simple post-processing to clean up potential repetitions or unwanted tokens
+    cleaned_text = text.replace(prompt, "").strip() # Remove the prompt from the output
+
+    # Remove consecutive repeated words - improved
+    words = cleaned_text.split()
+    if not words:
+        return ""
+    cleaned_words = [words[0]]
+    for i in range(1, len(words)):
+        if words[i].lower() != words[i-1].lower(): # Case-insensitive comparison
+            cleaned_words.append(words[i])
+    return " ".join(cleaned_words)
+
+
+def recommend_course(t):
+    t = t.lower()
+    if "python" in t: return "🐍 Python Programming – Beginner to Advanced"
+    if "data science" in t: return "📊 Data Science Master Program"
+    if "ai" in t or "machine learning" in t or "ml" in t: return "🤖 AI & Machine Learning with Real Projects"
+    if "web" in t or "full stack" in t or "javascript" in t or "react" in t: return "🌐 Full Stack Web Development"
+    if "java" in t: return "☕ Java Programming Essentials"
+    return None
+
+def chat(user_input, history):
+    en, lang = to_en(user_input)
+    course = recommend_course(en)
+    if course:
+        en_resp = f"I recommend you check out: {course}"
+    else:
+        # Modify prompt to encourage structured output based on keywords
+        prompt = f"User: {en}\nAssistant:"
+        if any(keyword in en.lower() for keyword in ["what is", "tell me about"]):
+             prompt = f"User: {en}\nAssistant: Here is information about {en.lower().replace('what is', '').replace('tell me about', '').strip()}:\n"
+        elif "recommend" in en.lower():
+             prompt = f"User: {en}\nAssistant: Based on your request, here is a recommendation:\n"
+
+
+        en_resp = generate(prompt)
+
+        # Apply post-processing to clean the generated text
+        en_resp = post_process_generated_text(en_resp, prompt)
+
+        if en_resp.startswith(prompt):
+            en_resp = en_resp[len(prompt):].strip()
+
+    final = from_en(en_resp, lang)
+    history = history + [(user_input, final)]
+    return history, history
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🌐 Multilingual GPT-2 Chatbot")
+    chatbot = gr.Chatbot(height=420)
+    msg = gr.Textbox(label="Your Message", placeholder="Type here...")
+    clear = gr.Button("🗑️ Clear")
+    state = gr.State([])
+    msg.submit(chat, [msg, state], [chatbot, state])
+    clear.click(lambda: ([], []), None, [chatbot, state], queue=False)
+
+# You can run this in a separate cell using !python app_gradio.py if needed,
+# but running it directly in the notebook cell is also possible.
+# if __name__ == "__main__":
+#     demo.launch()