Update app.py

app.py

@@ -1,237 +1,107 @@
 import gradio as gr
 import openai
+import time
 from transformers import pipeline
 from gtts import gTTS
-import time
 import os
 
-# OpenAI Chatbot Class
+# ✅ 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
 class OpenAIChatbot:
-    def __init__(self, api_key: str = None):
-        self.client = None
-        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!"])
+    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():
             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:
-            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(
+            response = openai.chat.completions.create(
                 model=self.model,
                 messages=messages,
-                max_tokens=1000,
+                stream=True,
                 temperature=0.7,
-                stream=True
+                max_tokens=1000
             )
-            
-            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]
+            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
                     yield history
                     time.sleep(0.02)
-                    
         except Exception as e:
-            history[-1] = [message, f"Error: {str(e)}"]
+            history[-1][1] = f"Error: {str(e)}"
             yield history
 
-# 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")
+chatbot = OpenAIChatbot()
 
-# Task functions
-def analyze_sentiment(text):
+# Multi-task handler
+def perform_task(task, text):
     if not text.strip():
-        return "Please enter text to analyze."
-    
-    result = sentiment_pipeline(text)[0]
-    return f"**Sentiment:** {result['label']}\n**Confidence:** {result['score']:.3f}"
+        return "⚠️ Please enter some text.", None, gr.update(visible=False)
 
-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']
+    if task == "Sentiment Analysis":
+        result = sentiment_pipeline(text)[0]
+        return f"Label: {result['label']} | Confidence: {round(result['score'], 3)}", None, gr.update(visible=False)
 
-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()
+    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)
+
+        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])
 
-# 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)
+demo.launch(share=True)