Create LLMwithvoice.py

LLMwithvoice.py

@@ -0,0 +1,46 @@
+import requests
+from IPython.display import Audio
+
+# Hugging Face API URL for Roberta model
+API_URL_ROBERTA = "https://api-inference.huggingface.co/models/deepset/roberta-base-squad2"
+# Hugging Face API URL for text-to-speech model
+API_URL_TTS = "https://api-inference.huggingface.co/models/suno/bark"
+
+def query_roberta(api_token, payload):
+    headers = {"Authorization": f"Bearer {api_token}"}
+    response = requests.post(API_URL_ROBERTA, headers=headers, json=payload)
+    try:
+        return response.json()
+    except ValueError:
+        return {"error": "Invalid JSON response"}
+
+def query_tts(api_token, payload):
+    headers = {"Authorization": f"Bearer {api_token}"}
+    response = requests.post(API_URL_TTS, headers=headers, json=payload)
+    return response.content
+
+def chat_with_roberta(api_token, question, context):
+    payload = {
+        "inputs": {
+            "question": question,
+            "context": context
+        }
+    }
+    response = query_roberta(api_token, payload)
+    if "error" in response:
+        return f"Error: {response['error']}"
+    else:
+        try:
+            return response['answer']
+        except (IndexError, KeyError):
+            return f"Unexpected response structure: {response}"
+
+def generate_speech(api_token, text):
+    payload = {"inputs": text}
+    audio_bytes = query_tts(api_token, payload)
+    return audio_bytes
+
+def gradio_interface(api_token, context, question):
+    answer = chat_with_roberta(api_token, question, context)
+    audio_bytes = generate_speech(api_token, answer)
+    return answer, Audio(audio_bytes, autoplay=True)