Update app.py

app.py

@@ -1,107 +1,239 @@
 import gradio as gr
 import openai
-import time
 from transformers import pipeline
 from gtts import gTTS
+import time
 import os
-
-# ✅ Set your OpenAI API key here
 openai.api_key = "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"  # <<< REPLACE THIS WITH YOUR KEY
 
-# Load pipelines
-sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-summarization_pipeline = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# Chatbot class
+# OpenAI Chatbot Class
 class OpenAIChatbot:
-    def __init__(self, model="gpt-3.5-turbo"):
-        self.model = model
-
-    def set_model(self, model_name):
-        self.model = model_name
-        return f"Model set to {model_name}"
-
-    def stream_chat(self, message, history, system_prompt=""):
-        if not message.strip():
+    def __init__(self, api_key: str = None):
+        self.client = openai
+        self.model = "gpt-3.5-turbo"
+        if api_key:
+            self.set_api_key(api_key)
+    
+    def set_api_key(self, api_key: str):
+        try:
+            self.client = openai.OpenAI(api_key=api_key)
+            self.client.models.list()
+            return "✅ API Key set successfully!"
+        except Exception as e:
+            return f"❌ Error: {str(e)}"
+    
+    def stream_chat(self, message: str, history: list, system_prompt: str = ""):
+        if not self.client:
+            history.append([message, "Please set your OpenAI API key first!"])
             yield history
             return
-
-        messages = [{"role": "system", "content": system_prompt}] if system_prompt else []
-        for user, bot in history:
-            messages += [{"role": "user", "content": user}, {"role": "assistant", "content": bot}]
-        messages.append({"role": "user", "content": message})
-
-        history.append([message, ""])
-
+        
         try:
-            response = openai.chat.completions.create(
+            messages = []
+            if system_prompt.strip():
+                messages.append({"role": "system", "content": system_prompt})
+            
+            for chat_pair in history:
+                if len(chat_pair) >= 2:
+                    messages.append({"role": "user", "content": chat_pair[0]})
+                    messages.append({"role": "assistant", "content": chat_pair[1]})
+            
+            messages.append({"role": "user", "content": message})
+            history.append([message, ""])
+            
+            stream = self.client.chat.completions.create(
                 model=self.model,
                 messages=messages,
-                stream=True,
+                max_tokens=1000,
                 temperature=0.7,
-                max_tokens=1000
+                stream=True
             )
-            bot_reply = ""
-            for chunk in response:
-                delta = chunk.choices[0].delta
-                if delta and delta.content:
-                    bot_reply += delta.content
-                    history[-1][1] = bot_reply
+            
+            bot_response = ""
+            for chunk in stream:
+                if chunk.choices[0].delta.content is not None:
+                    bot_response += chunk.choices[0].delta.content
+                    history[-1] = [message, bot_response]
                     yield history
                     time.sleep(0.02)
+                    
         except Exception as e:
-            history[-1][1] = f"Error: {str(e)}"
+            history[-1] = [message, f"Error: {str(e)}"]
             yield history
 
-chatbot = OpenAIChatbot()
+# Load Transformers models
+sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english") 
+summarization_pipeline = pipeline("summarization", model="RussianNLP/FRED-T5-Summarizer")
 
-# Multi-task handler
-def perform_task(task, text):
+# Task functions
+def analyze_sentiment(text):
     if not text.strip():
-        return "⚠️ Please enter some text.", None, gr.update(visible=False)
+        return "Please enter text to analyze."
+    
+    result = sentiment_pipeline(text)[0]
+    return f"**Sentiment:** {result['label']}\n**Confidence:** {result['score']:.3f}"
 
-    if task == "Sentiment Analysis":
-        result = sentiment_pipeline(text)[0]
-        return f"Label: {result['label']} | Confidence: {round(result['score'], 3)}", None, gr.update(visible=False)
-
-    elif task == "Summarization":
-        result = summarization_pipeline(text, max_length=100, min_length=30, do_sample=False)
-        return result[0]['summary_text'], None, gr.update(visible=False)
-
-    elif task == "Text-to-Speech":
-        tts = gTTS(text)
-        file_path = "tts_output.mp3"
-        tts.save(file_path)
-        return "Audio generated successfully.", file_path, gr.update(visible=True, value=file_path)
-
-# Interface
-with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 Multi-Task AI Assistant + OpenAI Chatbot")
-
-    with gr.Tab("AI Tasks"):
-        task = gr.Dropdown(["Sentiment Analysis", "Summarization", "Text-to-Speech"], value="Sentiment Analysis")
-        input_text = gr.Textbox(lines=6, label="Input")
-        run_btn = gr.Button("Run")
-        output = gr.Textbox(label="Result")
-        audio = gr.Audio(type="filepath", visible=False)
-
-        run_btn.click(perform_task, [task, input_text], [output, audio, audio])
-
-    with gr.Tab("Chatbot"):
-        model_select = gr.Dropdown(["gpt-3.5-turbo", "gpt-4"], value="gpt-3.5-turbo", label="Model")
-        system_prompt = gr.Textbox(label="System Prompt", placeholder="You are a helpful assistant...")
-        chat_ui = gr.Chatbot(label="Chat", height=400)
-        message_input = gr.Textbox(placeholder="Type your message...")
-        send_btn = gr.Button("Send")
-        clear_btn = gr.Button("Clear")
-
-        model_select.change(chatbot.set_model, inputs=[model_select], outputs=[])
-
-        def handle_chat(msg, hist, sys_prompt):
-            return chatbot.stream_chat(msg, hist, sys_prompt)
+def summarize_text(text):
+    if not text.strip():
+        return "Please enter text to summarize."
+    
+    result = summarization_pipeline(text, max_length=100, min_length=30, do_sample=False)
+    return result[0]['summary_text']
 
-        send_btn.click(handle_chat, [message_input, chat_ui, system_prompt], [chat_ui])
-        message_input.submit(handle_chat, [message_input, chat_ui, system_prompt], [chat_ui])
-        clear_btn.click(lambda: [], outputs=[chat_ui])
+def text_to_speech(text):
+    if not text.strip():
+        return "Please enter text for TTS.", None
+    
+    tts = gTTS(text)
+    filename = "tts_output.mp3"
+    tts.save(filename)
+    return f"Audio generated for {len(text.split())} words.", filename
+
+# Initialize chatbot
+chatbot = OpenAIChatbot()
 
-demo.launch(share=True)
+# Create interface
+def create_interface():
+    with gr.Blocks(title="AI Assistant") as demo:
+        gr.Markdown("# 🤖 Multi-Task AI Assistant")
+        
+        with gr.Tabs():
+            # OpenAI Chat Tab
+            with gr.TabItem("💬 OpenAI Chat"):
+                with gr.Row():
+                    api_key_input = gr.Textbox(
+                        label="OpenAI API Key",
+                        type="password",
+                        placeholder="sk-..."
+                    )
+                    set_key_btn = gr.Button("Set Key")
+                
+                status = gr.Textbox(label="Status", interactive=False)
+                
+                with gr.Row():
+                    model_dropdown = gr.Dropdown(
+                        choices=["gpt-3.5-turbo", "gpt-4"],
+                        value="gpt-3.5-turbo",
+                        label="Model"
+                    )
+                    system_prompt = gr.Textbox(
+                        label="System Prompt",
+                        placeholder="You are a helpful assistant..."
+                    )
+                
+                chatbot_interface = gr.Chatbot(label="Chat", height=400)
+                
+                with gr.Row():
+                    msg_input = gr.Textbox(
+                        placeholder="Type your message...",
+                        show_label=False,
+                        scale=4
+                    )
+                    send_btn = gr.Button("Send", variant="primary")
+                    clear_btn = gr.Button("Clear")
+            
+            # Sentiment Analysis Tab
+            with gr.TabItem("😊 Sentiment Analysis"):
+                with gr.Row():
+                    with gr.Column():
+                        sentiment_input = gr.Textbox(
+                            label="Text to analyze",
+                            lines=5,
+                            placeholder="Enter text to analyze sentiment..."
+                        )
+                        sentiment_btn = gr.Button("Analyze", variant="primary")
+                    
+                    with gr.Column():
+                        sentiment_output = gr.Textbox(
+                            label="Results",
+                            lines=5,
+                            interactive=False
+                        )
+            
+            # Summarization Tab
+            with gr.TabItem("📝 Summarization"):
+                with gr.Row():
+                    with gr.Column():
+                        summary_input = gr.Textbox(
+                            label="Text to summarize",
+                            lines=8,
+                            placeholder="Enter long text to summarize..."
+                        )
+                        summary_btn = gr.Button("Summarize", variant="primary")
+                    
+                    with gr.Column():
+                        summary_output = gr.Textbox(
+                            label="Summary",
+                            lines=8,
+                            interactive=False
+                        )
+            
+            # Text-to-Speech Tab
+            with gr.TabItem("🔊 Text-to-Speech"):
+                with gr.Row():
+                    with gr.Column():
+                        tts_input = gr.Textbox(
+                            label="Text to convert",
+                            lines=5,
+                            placeholder="Enter text to convert to speech..."
+                        )
+                        tts_btn = gr.Button("Generate Speech", variant="primary")
+                    
+                    with gr.Column():
+                        tts_status = gr.Textbox(label="Status", interactive=False)
+                        tts_audio = gr.Audio(label="Generated Audio")
+        
+        # Event handlers
+        def send_message(message, history, system_prompt):
+            if not message.strip():
+                return history, ""
+            
+            for updated_history in chatbot.stream_chat(message, history, system_prompt):
+                yield updated_history, ""
+        
+        # OpenAI Chat events
+        set_key_btn.click(
+            chatbot.set_api_key,
+            inputs=[api_key_input],
+            outputs=[status]
+        )
+        
+        send_btn.click(
+            send_message,
+            inputs=[msg_input, chatbot_interface, system_prompt],
+            outputs=[chatbot_interface, msg_input]
+        )
+        
+        msg_input.submit(
+            send_message,
+            inputs=[msg_input, chatbot_interface, system_prompt],
+            outputs=[chatbot_interface, msg_input]
+        )
+        
+        clear_btn.click(lambda: None, outputs=[chatbot_interface])
+        
+        # Other task events
+        sentiment_btn.click(
+            analyze_sentiment,
+            inputs=[sentiment_input],
+            outputs=[sentiment_output]
+        )
+        
+        summary_btn.click(
+            summarize_text,
+            inputs=[summary_input],
+            outputs=[summary_output]
+        )
+        
+        tts_btn.click(
+            text_to_speech,
+            inputs=[tts_input],
+            outputs=[tts_status, tts_audio]
+        )
+    
+    return demo
+
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch(share=True)