Update app.py

app.py

@@ -1,32 +1,45 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
 from gtts import gTTS
 import tempfile
+import torch
 
-# Load Hugging Face pipelines
+# ======= Sentiment Analysis =======
 sentiment_model = pipeline("sentiment-analysis")
-summarizer_model = pipeline("summarization")
 
-# Sentiment analysis function
-def analyze_sentiment(text):
-    result = sentiment_model(text)[0]
-    label = result['label']
-    score = round(result['score'], 2)
-    return f"Sentiment: {label}, Confidence: {score}"
+# ======= Summarization with tokenizer/model =======
+sum_model_name = "sshleifer/distilbart-xsum-12-6"
+sum_tokenizer = AutoTokenizer.from_pretrained(sum_model_name)
+sum_model = AutoModelForSeq2SeqLM.from_pretrained(sum_model_name)
 
-# Summarization function
 def summarize_text(text):
-    summary = summarizer_model(text, max_length=60, min_length=15, do_sample=False)
-    return summary[0]['summary_text']
+    inputs = sum_tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = sum_model.generate(inputs["input_ids"], max_length=60, min_length=15, do_sample=False)
+    return sum_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
 
-# Text-to-speech function
+# ======= Text to Speech =======
 def text_to_speech(text):
     tts = gTTS(text)
     with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
         tts.save(fp.name)
         return fp.name
 
-# Gradio app with separate tabs/pages
+# ======= Chatbot Tab =======
+chat_model_name = "microsoft/DialoGPT-medium"
+chat_tokenizer = AutoTokenizer.from_pretrained(chat_model_name)
+chat_model = AutoModelForCausalLM.from_pretrained(chat_model_name)
+chat_history = []
+
+def chat_with_bot(user_input):
+    global chat_history
+    new_input_ids = chat_tokenizer.encode(user_input + chat_tokenizer.eos_token, return_tensors='pt')
+    bot_input_ids = torch.cat(chat_history + [new_input_ids], dim=-1) if chat_history else new_input_ids
+    chat_history.append(new_input_ids)
+    response_ids = chat_model.generate(bot_input_ids, max_length=1000, pad_token_id=chat_tokenizer.eos_token_id)
+    response = chat_tokenizer.decode(response_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return response
+
+# ======= Gradio UI =======
 with gr.Blocks() as demo:
     gr.Markdown("## 📘 Homework - Tuwaiq Academy")
 
@@ -35,7 +48,8 @@ with gr.Blocks() as demo:
         input_sent = gr.Textbox(label="Enter your text", lines=6, placeholder="Type something...")
         output_sent = gr.Textbox(label="Sentiment Result")
         btn_sent = gr.Button("Analyze")
-        btn_sent.click(analyze_sentiment, inputs=input_sent, outputs=output_sent)
+        btn_sent.click(lambda text: sentiment_model(text)[0]['label'] + ", Confidence: " + str(round(sentiment_model(text)[0]['score'], 2)), 
+                       inputs=input_sent, outputs=output_sent)
 
     with gr.Tab("📝 Summarization"):
         gr.Markdown("### Summarize your text")
@@ -51,4 +65,11 @@ with gr.Blocks() as demo:
         btn_tts = gr.Button("Convert")
         btn_tts.click(text_to_speech, inputs=input_tts, outputs=output_audio)
 
+    with gr.Tab("🤖 Chatbot"):
+        gr.Markdown("### Chat with an AI Bot")
+        chat_input = gr.Textbox(label="You:", placeholder="Ask me anything...", lines=2)
+        chat_output = gr.Textbox(label="Bot:", lines=4)
+        btn_chat = gr.Button("Send")
+        btn_chat.click(chat_with_bot, inputs=chat_input, outputs=chat_output)
+
 demo.launch()