Create app.py

app.py

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+import gradio as gr
+import speech_recognition as sr
+from transformers import pipeline
+
+# Initialize the Hugging Face pipeline for speech-to-text
+speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-large")
+
+# Function to process user input (name and email)
+def capture_user_info(audio_input):
+    # Convert speech to text using Hugging Face's ASR model
+    name = speech_to_text(audio_input)["text"]
+    
+    # Now, ask for the email
+    email_prompt = "Please provide your email address."
+    
+    # Return the name and email prompt
+    return name, email_prompt
+
+# Function to capture email input after the name
+def capture_email(audio_input):
+    # Convert speech to text for the email
+    email = speech_to_text(audio_input)["text"]
+    return email
+
+# Define Gradio Interface
+def gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("### Welcome to Biryani Hub")
+        
+        with gr.Column():
+            gr.Markdown("#### Step 1: Tell me your name")
+            audio_input_name = gr.Audio(source="microphone", type="filepath", label="Speak your name")
+            name_output = gr.Textbox(label="Your Name:")
+            
+            # Step 1: Capture Name
+            audio_input_name.change(capture_user_info, inputs=audio_input_name, outputs=[name_output, email_prompt_output])
+            
+            gr.Markdown("#### Step 2: Please provide your email address")
+            audio_input_email = gr.Audio(source="microphone", type="filepath", label="Speak your email")
+            email_output = gr.Textbox(label="Your Email:")
+            
+            # Step 2: Capture Email
+            audio_input_email.change(capture_email, inputs=audio_input_email, outputs=email_output)
+
+    return demo
+
+# Launch the Gradio Interface
+demo = gradio_interface()
+demo.launch(debug=True)