Create app.py

app.py

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+import os
+# from dotenv import load_dotenv
+import streamlit as st
+import PIL.Image
+import google.generativeai as genai
+from langchain.prompts import ChatPromptTemplate
+from langchain_community.llms import Ollama
+from transformers import MllamaForConditionalGeneration, AutoProcessor
+import torch
+from accelerate import init_empty_weights
+# Load environment variables
+
+# Configure Gemini API
+# genai.configure(api_key=os.getenv("gkey2"))
+
+# Define the prompt template
+# prompt = ChatPromptTemplate.from_messages(
+#     [
+#         ("system", "You are a helpful assistant. Please respond to the user's queries."),
+#         ("user", "Question: {question}")
+#     ]
+# )
+
+prompt="<|image|><|begin_of_text|>You are a helpful assistant. Please respond to the user's queries."
+
+# Initialize the Llama model
+# model = Ollama(model="llama3.2")
+model_id = "meta-llama/Llama-3.2-11B-Vision"
+
+model = MllamaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+processor = AutoProcessor.from_pretrained(model_id)
+
+# Define function to get response from the model
+def get_gemin_response(input_text, img):
+    # complete_prompt = prompt.format(question=input_text)
+    inputs = processor(images=img, text=prompt, return_tensors="pt").to(model.device)
+    response=model.generate(**inputs, max_new_tokens=30)
+
+    # if input_text != "":
+    #     # Only generate content from input text if present
+    #     response = model.generate([input_text])
+    # else:
+    #     response = model.generate([img_text])
+    return response
+
+# Define the main function for the Streamlit app
+def main():
+    st.set_page_config(page_title='Gemini Image & Text')
+    st.header('Gemini LLM Application')
+
+    # Input text
+    input_text = st.text_input("Input :", key='input')
+
+    # Image uploader
+    imgupload = st.file_uploader('Choose an image file', type=['jpg', 'jpeg', 'png'])
+
+    # Display uploaded image and convert to text format (if needed)
+    img_text = ""
+    if imgupload is not None:
+        img = PIL.Image.open(imgupload)
+        st.image(img, caption='Uploaded Image', use_column_width=True)
+        img_text = "Image uploaded successfully."
+    if st.button('Generate Response'):
+        # Ensure both inputs are provided
+        if img is not None and input_text:  
+            # Get response from the model
+            response = get_gemin_response(input_text, img)
+            st.write(processor.decode(response[0]))
+        else:
+            st.error("Please provide both input text and an image before generating a response.")
+
+
+# Run the app
+if __name__ == "__main__":
+    main()