Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,92 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import openai 
+from dotenv import load_dotenv
+import os
+from groq import Groq
+import base64
+from PIL import Image
+import io
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# Set page config
+st.set_page_config(
+    page_title="Image Analysis with AI",
+    page_icon="🖼️",
+    layout="centered"
+)
+
+# Add title and description
+st.title("AI Image Analysis")
+st.markdown("Upload an image or provide an image URL to get AI-generated commentary.")
+
+# Load environment variables
+load_dotenv()
+
+# Initialize Groq client
+client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+
+# Create input field for image URL
+image_url = st.text_input("Enter Image URL", "https://static.seekingalpha.com/uploads/2016/1/19/saupload_fredgraph.jpg")
+
+# Add a file uploader
+uploaded_file = st.file_uploader("Or upload an image", type=["jpg", "jpeg", "png"])
+
+def get_image_url():
+    if uploaded_file is not None:
+        # Convert uploaded file to base64
+        image = Image.open(uploaded_file)
+        buffered = io.BytesIO()
+        image.save(buffered, format="PNG")
+        img_str = base64.b64encode(buffered.getvalue()).decode()
+        return f"data:image/png;base64,{img_str}"
+    return image_url
+
+# Add a button to trigger analysis
+if st.button("Analyze Image"):
+    try:
+        # Show loading spinner
+        with st.spinner("Analyzing image..."):
+            current_image_url = get_image_url()
+            
+            # Create the completion request
+            completion = client.chat.completions.create(
+                model="meta-llama/llama-4-scout-17b-16e-instruct",
+                messages=[
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": "Write a detailed commentary on the trend observed in the image?"
+                            },
+                            {
+                                "type": "image_url",
+                                "image_url": {
+                                    "url": current_image_url
+                                }
+                            }
+                        ]
+                    }
+                ],
+                temperature=1,
+                max_completion_tokens=300,
+                top_p=1,
+                stream=False,
+                stop=None,
+            )
+
+            # Display the image
+            if uploaded_file is not None:
+                st.image(uploaded_file, caption="Analyzed Image", use_column_width=True)
+            else:
+                st.image(image_url, caption="Analyzed Image", use_column_width=True)
+            
+            # Display the analysis
+            st.subheader("AI Analysis")
+            st.write(completion.choices[0].message.content)
+
+    except Exception as e:
+        st.error(f"An error occurred: {str(e)}")
+
+# Add footer
+st.markdown("---")
+st.markdown("Built with Streamlit and Groq AI")