Create app.py

app.py

@@ -0,0 +1,124 @@
+import gradio as gr
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import requests
+from bs4 import BeautifulSoup
+
+sentiment_pipeline = pipeline("sentiment-analysis") # 1-Sentiment Analysis Pipeline
+
+def get_sentiment(text):
+    result = sentiment_pipeline(text)[0]
+    sentiment = result['label']
+    confidence = result['score']
+    return sentiment, confidence
+########################################################
+
+chatbot_tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") # 2-Chatbot Pipeline
+
+chatbot_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+
+def generate_response(message, history):
+    # Encode the input message
+    input_ids = chatbot_tokenizer.encode(message + chatbot_tokenizer.eos_token, return_tensors="pt")
+    
+    # Generate response
+    response_ids = chatbot_model.generate(
+        input_ids,
+        max_length=1000,
+        pad_token_id=chatbot_tokenizer.eos_token_id,
+        no_repeat_ngram_size=3,
+        do_sample=True,
+        top_k=100,
+        top_p=0.7,
+        temperature=0.8
+    )
+    
+    # Decode the response
+    response = chatbot_tokenizer.decode(response_ids[0], skip_special_tokens=True)
+    return response
+
+########################################################
+
+summary_pipeline = pipeline("summarization", model="Falconsai/text_summarization") # 3-Summarization Pipeline
+
+def summarize_url(url):
+    try:
+        data = requests.get(url)
+        soup = BeautifulSoup(data.content, "html.parser")
+        article = soup.find("article")
+        if article:
+            text = article.text.strip()
+            summary = summary_pipeline(text, max_length=512, truncation=True)[0]['summary_text']
+            return summary
+        else:
+            return "Could not find an article on the provided URL."
+    except Exception as e:
+        return f"Error: {str(e)}"
+########################################################
+
+transcription_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-small") # 4-Speech Recognition Pipeline
+
+def transcribe_audio(audio_file):
+    try:
+        transcription = transcription_pipeline(audio_file)["text"]
+        return transcription
+    except Exception as e:
+        return f"Error during transcription: {str(e)}"
+########################################################
+
+
+with gr.Blocks() as interface:
+    gr.Markdown("# Multi-Model Model on Gardio") # Our Gradio Interface 
+
+    
+    with gr.Tabs():
+        with gr.Tab("Sentiment Analysis"):
+            gr.Markdown("Enter a sentence to analyze its sentiment and confidence score.")
+            text_input = gr.Textbox(label="Enter text")
+            sentiment_output = gr.Textbox(label='Sentiment')
+            confidence_output = gr.Textbox(label='Confidence Score')
+            analyze_button = gr.Button("Analyze")
+            analyze_button.click(get_sentiment, inputs=text_input, outputs=[sentiment_output, confidence_output])
+        
+        with gr.Tab("Summarization"):
+            gr.Markdown("Enter a news article URL to get a summary.")
+            url_input = gr.Textbox(label="Article URL")
+            summary_output = gr.Textbox(label="Summary", lines=5)
+            summarize_button = gr.Button("Summarize")
+            summarize_button.click(summarize_url, inputs=url_input, outputs=summary_output)
+            
+        with gr.Tab("Speech Recognition"):
+            gr.Markdown("Upload an audio file for transcription.")
+            audio_input = gr.Audio(label="Upload Audio", type="filepath")
+            transcription_output = gr.Textbox(label="Transcription", lines=3)
+            transcribe_button = gr.Button("Transcribe")
+            transcribe_button.click(transcribe_audio, inputs=audio_input, outputs=transcription_output)
+        
+        with gr.Tab("Chatbot"):
+            gr.Markdown("Have a conversation with the AI chatbot.")
+            chatbot = gr.Chatbot(
+                label="Chat History",
+                height=400
+            )
+            msg = gr.Textbox(
+                label="Type your message",
+                placeholder="Type your message here...",
+                show_label=False
+            )
+            clear = gr.Button("Clear")
+
+            def user(user_message, history):
+                return "", history + [[user_message, None]]
+
+            def bot(history):
+                user_message = history[-1][0]
+                bot_message = generate_response(user_message, history)
+                history[-1][1] = bot_message
+                return history
+
+            msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+                bot, chatbot, chatbot
+            )
+            clear.click(lambda: None, None, chatbot, queue=False)
+
+if __name__ =="__main__":               ## running my app on hugging face
+    interface.launch()