Update app.py

app.py

@@ -4,7 +4,53 @@ from st_audiorec import st_audiorec
 from gtts import gTTS
 import os
 
-# ... (previous code remains unchanged)
+# 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.
+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/")
+
+
+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"
+    )
+
+# 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):
+    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"
+        )
+        st.write("Done")
+        return response
 
 # Function to convert text to speech
 def text_to_speech(text, language='en', filename='output.mp3'):
@@ -17,7 +63,40 @@ def text_to_speech(text, language='en', filename='output.mp3'):
     # Play the audio file
     os.system(f'start {filename}')  # This works on Windows. For other OS, you might need a different command.
 
-# ... (previous code remains unchanged)
+# Streamlit UI
+st.title(TITLE)
+st.write(DESCRIPTION)
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"], avatar=("🧑‍💻" if message["role"] == 'human' else '🦙')):
+        st.markdown(message["content"])
+
+textinput = st.chat_input("Ask AgriTure anything...")
+wav_audio_data = st_audiorec()
+
+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.session_state.messages.append({"role": "human", "content": prompt})
+
+    # transcribe audio
+    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})
 
 # React to user input
 if prompt := textinput:
@@ -26,14 +105,13 @@ if prompt := textinput:
     # Add user message to chat history
     st.session_state.messages.append({"role": "human", "content": prompt})
 
-    # Update the global response variable
     response = predict(message=prompt)
-
+    
     # 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)  # Call text_to_speech after getting the response