added mcp logic

app.py

@@ -1,26 +1,64 @@
 import gradio as gr
 from dotenv import load_dotenv
 from openai import OpenAI
+from duckduckgo_search import DDGS
 import os
+import requests
+import json
+import re
 
+# Load environment variables
 load_dotenv()
 
 API_KEY = os.getenv("OPENAI_API_KEY")
 MODEL_NAME = os.getenv("MODEL_NAME")
 BASE_URL = os.getenv("BASE_URL")
+IMAGE_SEARCH_ENDPOINT = os.getenv("IMAGE_SEARCH_ENDPOINT")
 
 client = OpenAI(
     api_key=API_KEY,
     base_url=BASE_URL,
 )
 
+# MCP TOOL: Search via DuckDuckGo
+def search(query: str) -> list[dict]:
+    with DDGS() as ddgs:
+        results = ddgs.text(query, max_results=5)
+    return results
 
-def chat_with_gemma(history, message):
-    messages = [{"role": "system", "content": "you are an helpful assistant who helps users to find products on the internet"}]
+# MCP TOOL: Analyze Image URL to get caption for further searching
+def analyze_image(data: str) -> str:
+    try:
+        response = requests.post(IMAGE_SEARCH_ENDPOINT, json={"image_url": data})
+        if response.status_code == 200:
+            return response.json().get("caption", "No caption found")
+        else:
+            return f"Image analysis failed: {response.status_code}"
+    except Exception as e:
+        return f"Error during image analysis: {str(e)}"
+
+# Helper to extract tool_code from model response
+def extract_tool_code(text):
+    match = re.search(r"```tool_code\\n(.*?)```", text, re.DOTALL)
+    return match.group(1).strip() if match else None
+
+# Helper to format tool output back to model
+def format_tool_output(output):
+    return f"```tool_output\n{json.dumps(output)}\n```"
+
+# CHAT HANDLER
+def chat_with_gemma(history, message, image_url):
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant who helps users find products online using search and image analysis. Wrap tool use in ```tool_code``` and return results in ```tool_output```."}  ]
     for user_msg, bot_msg in history:
         messages.append({"role": "user", "content": user_msg})
         messages.append({"role": "assistant", "content": bot_msg})
-        messages.append({"role": "user", "content": message})
+
+    if image_url:
+        image_caption = analyze_image(image_url)
+        message = f"Image URL: {image_url}\nCaption: {image_caption}\nUser says: {message}"
+
+    messages.append({"role": "user", "content": message})
 
     try:
         response = client.chat.completions.create(
@@ -29,29 +67,52 @@ def chat_with_gemma(history, message):
             temperature=0.7,
             max_tokens=512,
         )
+
         reply = response.choices[0].message.content.strip()
+
+        tool_code = extract_tool_code(reply)
+        if tool_code:
+            tool_result = eval(tool_code)  # Note: Only safe in dev/testing
+            tool_output = format_tool_output(tool_result)
+
+            messages.append({"role": "user", "content": tool_output})
+
+            response2 = client.chat.completions.create(
+                model=MODEL_NAME,
+                messages=messages,
+                temperature=0.7,
+                max_tokens=512,
+            )
+
+            reply = response2.choices[0].message.content.strip()
+
     except Exception as e:
         reply = f"⚠️ Error: {str(e)}"
 
-    # Return updated history
     history.append((message, reply))
-    return history, ""
+    return history, "", ""
 
+# GRADIO UI
+with gr.Blocks(title="🧠 Gemma Product Finder - MCP Tool", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+        <h1 style='text-align: center; color: #4e73df;'>🛍️ Gemma Product Finder</h1>
+        <p style='text-align: center; color: #6c757d;'>Find Amazon & Flipkart products with AI</p>
+    """)
 
-with gr.Blocks(title="Gemma Chat - MCP Tool") as demo:
-    gr.Markdown("## 🧠 Gemma LLM Chatbot via Modal (MCP Tool)")
     with gr.Row():
-        chatbot = gr.Chatbot()
+        chatbot = gr.Chatbot(height=420, label="🧠 Chat with Gemma", bubble_full_width=False)
+
     with gr.Row():
-        msg = gr.Textbox(label="Your message here...", placeholder="Ask something...")
+        msg = gr.Textbox(label="💬 Ask something", placeholder="e.g. Red Nike shoes under 4000", scale=3)
+        image_url = gr.Textbox(label="🌆 Optional image URL", placeholder="Paste image URL here", scale=2)
+
     with gr.Row():
-        clear = gr.Button("🔄 Clear Chat")
+        clear = gr.Button("🔄 Clear Chat", variant="secondary")
 
     state = gr.State([])
 
-    msg.submit(chat_with_gemma, [state, msg], [chatbot, msg])
-    clear.click(lambda: ([], ""), outputs=[chatbot, msg])
+    msg.submit(chat_with_gemma, [state, msg, image_url], [chatbot, msg, image_url])
+    clear.click(lambda: ([], "", ""), outputs=[chatbot, msg, image_url])
 
-# ✅ Launch the app
 if __name__ == "__main__":
-    demo.launch(debug=True)
+    demo.launch(mcp_server=True)

requirements.txt

@@ -1,4 +1,6 @@
 gradio[mcp]
 modal
 openai
-python-dotenv
+python-dotenv
+duckduckgo_search
+requests