Create app.py

app.py

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+import streamlit as st
+import os
+from PIL import Image
+import tempfile
+
+import google.generativeai as genai
+secret_key = os.getenv("SECRET_KEY")
+
+genai.configure(api_key=secret_key)
+model2=genai.GenerativeModel('gemini-pro')
+
+st.title('Mental Health')
+video_url1= 'https://youtu.be/NQcYZplTXnQ?si=egutHE1H9YwQNk_I'
+st.header("Video Demo")
+st.video(video_url1)
+
+from transformers import pipeline
+classifier = pipeline("text-classification")
+input1 = st.text_input('Enter text here:', '')
+
+
+if input1:
+    outputs1 = classifier(input1)
+    st.write('You entered:', input1)
+    st.write('Emotion:', outputs1[0]['label'])
+    st.write('Confidence in Emotion:', outputs1[0]['score'])
+    
+if 'chat' not in st.session_state:
+    st.session_state.chat=model2.start_chat(history=[])
+    
+def role_to_streamlit(role):
+    if role=='model':
+        return 'assistant'
+    else:
+        return role
+        
+for message in st.session_state.chat.history:
+    with st.chat_message(role_to_streamlit(message.role)):
+        st.markdown(message.parts[0].text)
+
+if prompt2 := st.chat_input('Write your problem'):
+    st.chat_message('user').markdown(prompt2)
+    response2=st.session_state.chat.send_message(prompt2)
+    with st.chat_message('assistant'):
+        st.markdown(response2.text)
+
+def get_gemini_response(input,image):
+    model = genai.GenerativeModel('gemini-pro-vision')
+    if input!="":
+       response = model.generate_content([input,image])
+    else:
+       response = model.generate_content(image)
+    return response.text
+
+input3='Tell me about the emotion shown in the image'
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+image=""   
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image.", use_column_width=True)
+submit=st.button("Tell me about the emotion in image")
+if submit:
+    response=get_gemini_response(input3,image)
+    st.subheader("The Response is")
+    st.write(response)
+
+
+audio_prompt="""you are audio emotion detector.You will be taking the audio
+and finding the emotion in audio. Please provide the emotion of the audio given here:  """
+
+audio_text=st.text_input("What do you want to know about the audio:")
+
+if audio_text:
+
+    audio_prompt=""".You will be analyse the audio and provide the answers of the question given here:  """+text
+
+
+audio_file = st.file_uploader("Upload an audio file", type=["mp3", "wav", "ogg"])
+
+def generate_gemini_content(prompt,audio_file):
+
+    with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as tmp_file:
+        tmp_file.write(audio_file.getvalue())
+        tmp_file.close()  # close the file
+
+        model = genai.GenerativeModel("models/gemini-1.5-pro-latest")
+        your_file = genai.upload_file(tmp_file.name)
+        response = model.generate_content([prompt, your_file])
+        return response.text
+
+    os.remove(tmp_file.name)
+
+if st.button("Answer or summary"):
+
+    if audio_file:
+        summary=generate_gemini_content(prompt,audio_file)
+        st.markdown("## Summary:")
+        st.write(summary)