Update app.py

app.py

@@ -1,8 +1,11 @@
 import streamlit as st
 from gradio_client import Client
+from st_audiorec import st_audiorec
 from gtts import gTTS
 import os
 
+
+
 # Constants
 TITLE = "AgriTure"
 DESCRIPTION = """
@@ -14,6 +17,8 @@ Hope this will be a Successful Project!!!
 """
 
 # 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)
@@ -25,28 +30,29 @@ whisper_client = Client("https://sanchit-gandhi-whisper-large-v2.hf.space/")
 
 
 def transcribe(wav_path):
+    
     return whisper_client.predict(
-        wav_path,
-        "transcribe",
-        api_name="/predict"
+				wav_path,	# str (filepath or URL to file) in 'inputs' Audio component
+				"transcribe",	# str in 'Task' Radio component
+				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 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):
     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,
-            system_prompt,
-            max_new_tokens,
-            temperature,
-            Topp,
-            500,
-            Repetitionpenalty,
-            api_name="/chat"
+    			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"
         )
         st.write("Done")
         return response
@@ -56,12 +62,15 @@ 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}')
-
+    
 # Streamlit UI
 st.title(TITLE)
 st.write(DESCRIPTION)
 
-# Initialize session state
+
+
+
+
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
@@ -71,40 +80,41 @@ for message in st.session_state.messages:
         st.markdown(message["content"])
 
 textinput = st.chat_input("Ask AgriTure anything...")
+wav_audio_data = st_audiorec()
 
-# Assume st_audiorec is a simplified audio recorder function
-wav_audio_data = st.audio_recorder()
-
-if wav_audio_data is not None:
+if wav_audio_data != 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='🦙'):
         st.markdown(response)
+    # Add assistant response to chat history
     st.session_state.messages.append({"role": "assistant", "content": response})
 
-    # Convert response to audio
-    text_to_speech(response)
-
 # React to user input
 if prompt := textinput:
-    st.chat_message("human", avatar="💬: ").markdown(prompt)
+    # Display user message in chat message container
+    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)
-
+    response = predict(message=prompt)#, temperature= temperatureSide,max_new_tokens=max_new_tokensSide, Topp=ToppSide,Repetitionpenalty=RepetitionpenaltySide)
+    # Display assistant response in chat message container
     with st.chat_message("assistant", avatar='🦙'):
         st.markdown(response)
+    # Add assistant response to chat history
     st.session_state.messages.append({"role": "assistant", "content": response})
 
-    # Convert response to audio
-    text_to_speech(response)
+# Convert response to speech
+text_to_speech(response)