Upload s.py

s.py

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+
+
+from flask import Flask, request, jsonify
+import os
+from langchain.prompts import ChatPromptTemplate
+from langchain.agents import Tool, create_openai_tools_agent, AgentExecutor
+from langchain_core.prompts import MessagesPlaceholder
+from skills.vision import get_chat_completion  
+from skills.serch import search_tool  
+from skills.wiki import wikipedia_tool  
+from langchain_groq import ChatGroq
+
+# Initialize the Groq API key
+GROQ_API_KEY = "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"  # Replace with your actual API key
+os.environ["GROQ_API_KEY"] = GROQ_API_KEY
+
+# Initialize the Groq LLM
+llm = ChatGroq(
+    model_name="mixtral-8x7b-32768",
+    temperature=0.7,
+    max_tokens=4096
+)
+
+def analyse(query):
+    
+    response = get_chat_completion(query,image_file)
+    return response
+
+
+# Initialize external tools
+tools = [
+    Tool(name="analyseimage", func=analyse, description="Tool for image vision LLM model queries"),
+    Tool(name="Search", func=search_tool, description="Search the internet for current information"),
+    Tool(name="Wikipedia", func=wikipedia_tool, description="Query Wikipedia for detailed topic information"),
+]
+
+# Create the agent prompt
+template_messages = [
+    ("system", "You are an AI assistant capable of using tools to analyze images, search the internet, and query Wikipedia."),
+    ("user", "{input}"),
+    MessagesPlaceholder(variable_name="agent_scratchpad"),
+]
+prompt = ChatPromptTemplate.from_messages(template_messages)
+
+# Create the agent
+agent = create_openai_tools_agent(llm=llm, tools=tools, prompt=prompt)
+
+# Create the agent executor
+agent_executor = AgentExecutor(
+    agent=agent,
+    tools=tools,
+    verbose=True,
+    handle_parsing_errors=True
+)
+
+# Initialize Flask app
+app = Flask(__name__)
+
+@app.route('/analyze', methods=['POST'])
+def analyze():
+    global image_file
+    global query
+    try:
+        query = request.form.get('query')
+        if not query:
+            return jsonify({"error": "Query is required"}), 400
+        image_file = request.files.get('image')
+        
+        response = agent_executor.invoke({"input": query})
+        
+        return jsonify({"response": response["output"]})
+
+    except Exception as e:
+        return jsonify({"error": f"Server error: {str(e)}"}), 500
+
+# CORS Headers
+@app.after_request
+def after_request(response):
+    response.headers.add('Access-Control-Allow-Origin', '*')
+    response.headers.add('Access-Control-Allow-Headers', 'Content-Type')
+    response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
+    return response
+
+if __name__ == "__main__":
+    app.run(debug=True, host='0.0.0.0', port=5000)