Update app.py

app.py

@@ -4,77 +4,57 @@ from st_audiorec import st_audiorec
 from gtts import gTTS
 import os
 
-
-
 # Constants
 TITLE = "AgriTure"
 DESCRIPTION = """
 ----
-This Project demonstrates a model fine-tuned by Achyuth. This Model is named as "AgriTure". This Model helps the farmers and scientists to develop the art of agriculture and farming.
+This Project demonstrates a model fine-tuned by Achyuth. This Model is named as "AgriaTure". This Model helps the farmers and scientists to develop the art of agriculture and farming.
 Hope this will be a Successful Project!!!
 ~Achyuth
 ----
 """
 
 # Initialize client
-
-
-with st.sidebar:
-    system_promptSide = st.text_input("Optional system prompt:")
-    temperatureSide = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05)
-    max_new_tokensSide = st.slider("Max new tokens", min_value=0.0, max_value=4096.0, value=4096.0, step=64.0)
-    ToppSide = st.slider("Top-p (nucleus sampling)", min_value=0.0, max_value=1.0, value=0.6, step=0.05)
-    RepetitionpenaltySide = st.slider("Repetition penalty", min_value=0.0, max_value=2.0, value=1.2, step=0.05)
-
 whisper_client = Client("https://sanchit-gandhi-whisper-large-v2.hf.space/")
 
+# Function to convert text to speech using gTTS
+def text_to_speech(text, lang='en'):
+    tts = gTTS(text=text, lang=lang, slow=False)
+    tts.save("response.mp3")
+    return "response.mp3"
 
+# Function to transcribe audio
 def transcribe(wav_path):
-    
     return whisper_client.predict(
-				wav_path,	# str (filepath or URL to file) in 'inputs' Audio component
-				"transcribe",	# str in 'Task' Radio component
-				api_name="/predict"
+        wav_path,
+        "transcribe",
+        api_name="/predict"
     )
 
 # Prediction function
-def predict(message, system_prompt='Your name is OpenGPT. You are developed by Achyuth. You need to mostly focus on giving information about future agriculture and advanced farming. Empower yourself farming future with cutting-edge technology and sustainable practices. You need to cultivate a greener and more productive. Your developer is studying in The Hyderabad Public School Kadapa.', temperature=0.7, max_new_tokens=4096,Topp=0.5,Repetitionpenalty=1.2):
+def predict(message, system_prompt='Your name is AgriaTure...', temperature=0.7, max_new_tokens=4096, Topp=0.5, Repetitionpenalty=1.2):
     with st.status("Starting client"):
         client = Client("https://huggingface-projects-llama-2-7b-chat.hf.space/")
         st.write("Requesting Audio Transcriber")
     with st.status("Requesting AgriTure v1"):
         st.write("Requesting API")
         response = client.predict(
-    			message,	# str in 'Message' Textbox component
-                system_prompt,	# str in 'Optional system prompt' Textbox component
-    			max_new_tokens,	# int | float (numeric value between 0 and 4096)
-    			temperature,	# int | float (numeric value between 0.0 and 1.0)
-    			Topp,	
-                500,
-    			Repetitionpenalty,	# int | float (numeric value between 1.0 and 2.0)
-    			api_name="/chat"
+            message,
+            system_prompt,
+            max_new_tokens,
+            temperature,
+            Topp,
+            500,
+            Repetitionpenalty,
+            api_name="/chat"
         )
         st.write("Done")
         return response
-        
-
-# Function to convert text to speech
-def text_to_speech(text, language='en', filename='output.mp3'):
-    tts = gTTS(text=text, lang=language, slow=False)
-    tts.save(filename)
-    os.system(f'start {filename}')
-    # Convert response to speech
-response=response
-text_to_speech(response)
-    
+
 # Streamlit UI
 st.title(TITLE)
 st.write(DESCRIPTION)
 
-
-
-
-
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
@@ -86,36 +66,50 @@ for message in st.session_state.messages:
 textinput = st.chat_input("Ask AgriTure anything...")
 wav_audio_data = st_audiorec()
 
-if wav_audio_data != None:
+if wav_audio_data is not None:
     with st.status("Transcribing audio..."):
         # save audio
         with open("audio.wav", "wb") as f:
             f.write(wav_audio_data)
         prompt = transcribe("audio.wav")
-       
         st.write("Transcribed Given Audio ✔")
-        
-    st.chat_message("human",avatar = "🧑‍💻").markdown(prompt)
+
+    st.chat_message("human", avatar="🌿").markdown(prompt)
     st.session_state.messages.append({"role": "human", "content": prompt})
 
     # transcribe audio
-    response = predict(message= prompt)
+    response = predict(message=prompt)
 
-    with st.chat_message("assistant", avatar='🦙'):
+    with st.chat_message("assistant", avatar='🌿'):
         st.markdown(response)
+
+    # Convert AI response to speech
+    speech_file = text_to_speech(response)
+
+    # Play the generated speech
+    st.audio(speech_file, format="audio/mp3")
+
     # Add assistant response to chat history
     st.session_state.messages.append({"role": "assistant", "content": response})
 
 # React to user input
 if prompt := textinput:
     # Display user message in chat message container
-    st.chat_message("human",avatar = "💬: ").markdown(prompt)
+    st.chat_message("human", avatar="🌿").markdown(prompt)
     # Add user message to chat history
     st.session_state.messages.append({"role": "human", "content": prompt})
 
-    response = predict(message=prompt)#, temperature= temperatureSide,max_new_tokens=max_new_tokensSide, Topp=ToppSide,Repetitionpenalty=RepetitionpenaltySide)
+    response = predict(message=prompt)
+
+    # Convert AI response to speech
+    speech_file = text_to_speech(response)
+
     # Display assistant response in chat message container
-    with st.chat_message("assistant", avatar='🦙'):
+    with st.chat_message("assistant", avatar='🌿'):
         st.markdown(response)
+
+    # Play the generated speech
+    st.audio(speech_file, format="audio/mp3")
+
     # Add assistant response to chat history
     st.session_state.messages.append({"role": "assistant", "content": response})