Initial Commit

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+import io
+import base64
+from PIL import Image
+from groq import Groq
+
+# Define the function to encode the image
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode("utf-8")
+
+# Function to run the user input code and display the plot
+def run_code(code, groq_api_key):
+    try:
+        # Setup Groq API client with the provided API key
+        llava_client = Groq(api_key=groq_api_key)
+        llama_client = Groq(api_key=groq_api_key)
+
+        fig, ax = plt.subplots()
+
+        # Create a safe environment to execute code
+        exec(code, {"plt": plt, "ax": ax})
+
+        # Save the plot to a byte buffer
+        buf = io.BytesIO()
+        plt.savefig(buf, format="png", bbox_inches="tight")
+        buf.seek(0)
+
+        # Open the saved image and resize it if necessary
+        img = Image.open(buf)
+        max_width, max_height = 600, 400  # Set maximum display size
+        img.thumbnail((max_width, max_height))  # Resize to fit
+
+        # Save the image to the disk
+        img.save("plot.png")
+        buf.seek(0)
+
+        # Encode the image for Groq API
+        base64_image = encode_image("plot.png")
+
+        # Sending the plot image to LLava API to get the description
+        llava_completion = llava_client.chat.completions.create(
+            model="llava-v1.5-7b-4096-preview",
+            messages=[{
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": f"Describe the plot values with image and the code provided to you. Code: {code}\n"},
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": f"data:image/png;base64,{base64_image}"}
+                    },
+                ],
+            }],
+            temperature=0.5,
+            max_tokens=4096,
+            top_p=1,
+            stream=False,
+            stop=None,
+        )
+
+        # Extract the LLava description from the API response
+        llava_description = llava_completion.choices[0].message.content
+
+        # Sending the plot image to Llama 3.1 API to get the description
+        llama_completion = llama_client.chat.completions.create(
+            model="llama-3.1-70b-versatile",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "What are the details of the plot provided by the user. Point out the important things in the dataset. What is the purpose of this dataset and how to interpret it. Analyze it."
+                },
+                {
+                    "role": "user",
+                    "content": code
+                }
+            ],
+            temperature=0,
+            max_tokens=4096,
+            top_p=1,
+            stream=True,
+            stop=None,
+        )
+
+        # Extract the Llama 3.1 description from the API response
+        llama_description = ""
+        for chunk in llama_completion:
+            llama_description += chunk.choices[0].delta.content or ""
+
+        return img, llava_description, llama_description
+
+    except Exception as e:
+        return None, f"Error: {str(e)}", None
+    finally:
+        plt.close(fig)
+
+# Define the Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""## Plot and Describe
+
+    **⚠️ Disclaimer:** Generative models may hallucinate or produce incorrect outputs. This tool is built for demonstration purposes only and should not be relied upon for critical analysis or decision-making.
+    """, elem_id="disclaimer")
+
+    with gr.Row():
+        api_key_input = gr.Textbox(
+            label="Groq API Key", type="password", placeholder="Enter your Groq API key here"
+        )
+
+    with gr.Row():
+        code_input = gr.Code(
+            language="python", lines=20, label="Input Code"
+        )
+        output_image = gr.Image(type="pil", label="Chart will be displayed here")
+
+    submit_btn = gr.Button("Submit")
+
+    with gr.Row():
+        output_llava_text = gr.Textbox(label="Description from LLaVa", interactive=False)
+        output_llama_text = gr.Textbox(label="Description from Llama 3.1", interactive=False)
+
+    submit_btn.click(
+        fn=run_code,
+        inputs=[code_input, api_key_input],
+        outputs=[output_image, output_llava_text, output_llama_text]
+    )
+
+# Launch the interface
+demo.launch()

requirements.txt

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+gradio==3.42.0
+matplotlib==3.7.2
+pillow==9.5.0
+groq