Upload app.py

app.py

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+from transformers import pipeline
+from openai import OpenAI
+import gradio as gr
+import os
+import dotenv
+
+
+
+api_key = os.getenv("OPENROUTER_API_KEY")
+
+client = OpenAI(
+    base_url="https://openrouter.ai/api/v1",
+    api_key=api_key,
+)
+
+
+asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
+
+
+def speech_to_text(speech):
+    text = asr(speech)["text"]
+    text = text.lower()
+    return text
+
+def chatbot(message, history):
+    messages = []
+    for human, assistant in history:
+        messages.append({"role": "user", "content": human})
+        messages.append({"role": "assistant", "content": assistant})
+    messages.append({"role": "user", "content": message})
+
+    response = client.chat.completions.create(
+        model="meta-llama/llama-3.1-405b-instruct:free",
+        messages=messages,
+        temperature=0.7,
+        max_tokens=1000
+    )
+    return response.choices[0].message.content
+    
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        audio_file = gr.Audio(type="filepath", label="Sube tu archivo de audio")
+
+        text_input = gr.Textbox(label="Texto transcrito (o ingresa tu mensaje)", placeholder="El texto aparecerá aquí...")
+        text_output = gr.Textbox(label="Respuesta del Chatbot", placeholder="La respuesta del chatbot aparecerá aquí...", interactive=False)
+
+        b1 = gr.Button("Reconocer Audio y Preguntar al Chatbot")
+
+    def process_audio_and_chat(audio_input, history):
+        if audio_input:
+
+            audio_text = speech_to_text(audio_input)
+
+            response = chatbot(audio_text, history)
+            print(response)
+            return audio_text, response
+            
+        return "", "Por favor, sube un archivo de audio."
+
+
+    b1.click(process_audio_and_chat, inputs=[audio_file, text_input], outputs=[text_input, text_output])
+
+
+demo.launch(debug=True)