init

.env

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+OPENROUTER_API_KEY="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"
+BASE_URL="https://openrouter.ai/api/v1"
+DATA="data/data.csv"

app.py

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+from models import *
+import gradio as gr
+
+GPT_4 = "deepseek/deepseek-chat-v3-0324:free" #12
+PHI_4 = "microsoft/phi-4" #2
+PHI_3 = "microsoft/phi-3-medium-128k-instruct:free" #3
+GEMMA_3_27B = "google/gemma-3-27b-it:free" #4
+GEMIN_FLASH_THINK = "google/gemini-2.0-flash-thinking-exp:free" #6
+GEMIN_FLASH = "google/gemini-flash-1.5-8b-exp" #7
+QWEN_32B = "qwen/qwq-32b:free" #8
+QWEN_25 = "qwen/qwen2.5-vl-72b-instruct:free" #10 #error
+DEEPSEEK_R1 = "deepseek/deepseek-r1:free" #11
+DEEPSEEK_R1_ZERO = "deepseek/deepseek-r1-zero:free" #13
+META_LLAMA_MODEL = "meta-llama/Llama-3.3-70B-Instruct:free" #14
+MISTRAL_SMALL_MODEL = "mistralai/mistral-small-3.1-24b-instruct:free" #15
+MISTRAL_NEMO = "mistralai/mistral-nemo:free" #16
+ZEPHYR = "huggingfaceh4/zephyr-7b-beta:free" #17
+OLYMPIC_CODER = "open-r1/olympiccoder-32b:free" #19
+LEARN = "google/learnlm-1.5-pro-experimental:free" #20
+REKA_FLASH = "rekaai/reka-flash-3:free" #21
+OPEN_CHAT = "openchat/openchat-7b:free" #21
+TOPPY = "undi95/toppy-m-7b:free" #23
+MOONLIGHT = "moonshotai/moonlight-16b-a3b-instruct:free"
+
+CONCISE_ENGLISH_PROMPT = "Answer in short and precise English sentences."
+
+def get_model(title, dev, model, name, user_input, system_prompt):
+    if user_input.lower() == "data":
+        df = get_data()
+        return df
+    
+    if user_input.lower() == "text":
+        text = get_text()
+        return text
+
+    if name == "" or name is None:
+        return "Enter Your Name !"
+        
+    if model is None or model == "":
+        return "Select AI Model !"
+
+    chain = ModelChain()
+    prompt = system_prompt + " " + CONCISE_ENGLISH_PROMPT
+
+    # Check the model and map to the correct model
+    if "ChatGPT" == model: #1
+        return chain.generate_response(GPT_4, name, user_input, prompt)
+    elif "Phi-4" == model: #2
+        return chain.generate_response(PHI_4, name, user_input, prompt)
+    elif "Phi-3" == model: #3
+        return chain.generate_response(PHI_3, name, user_input, prompt)
+    elif "Gemma-3" == model: #4
+        return chain.generate_response(GEMMA_3_27B, name, user_input, prompt)
+    elif "Gemini-2-Flash-Think" == model: #6
+        return chain.generate_response(GEMIN_FLASH_THINK, name, user_input, prompt)
+    elif "Gemini-Flash" == model: #7
+        return chain.generate_response(GEMIN_FLASH, name, user_input, prompt)
+    elif "QwQ-32B" == model: #8
+        return chain.generate_response(QWEN_32B, name, user_input, prompt)
+    elif "Qwen2.5" == model: #10
+        return chain.generate_response(QWEN_25, name, user_input, prompt)
+    elif "DeepSeek-R1" == model: #11
+        return chain.generate_response(DEEPSEEK_R1, name, user_input, prompt)
+    elif "DeepSeek-R1-Zero" == model: #11
+        return chain.generate_response(DEEPSEEK_R1, name, user_input, prompt)
+    elif "Llama-3.3" == model: #14
+        return chain.generate_response(META_LLAMA_MODEL, name, user_input, prompt)
+    elif "Mistral-Small" == model: #15
+        return chain.generate_response(MISTRAL_SMALL_MODEL, name, user_input, prompt)
+    elif "Mistral-Nemo" == model: #16
+        return chain.generate_response(MISTRAL_NEMO, name, user_input, prompt)
+    elif "Zephyr" == model: #17
+        return chain.generate_response(ZEPHYR, name, user_input, prompt)
+    elif "Olympic-Coder" == model: #19
+        return chain.generate_response(OLYMPIC_CODER, name, user_input, prompt)
+    elif "LearnLM" == model: #20
+        return chain.generate_response(LEARN, name, user_input, prompt)
+    elif "Reka-Flash" == model: #21
+        return chain.generate_response(REKA_FLASH, name, user_input, prompt)
+    elif "OpenChat" == model: #21
+        return chain.generate_response(OPEN_CHAT, name, user_input, prompt)
+    elif "Toppy" == model: #21
+        return chain.generate_response(TOPPY, name, user_input, prompt)
+    elif "MoonLight" == model: #21
+        return chain.generate_response(MOONLIGHT, name, user_input, prompt)
+    else:
+        return "Invalid Model Name : " + model
+
+def main():
+    view = gr.Interface(
+        fn= get_model,
+        inputs = [
+            gr.Markdown("# Switch AI"),
+            gr.Markdown("### by Kalash"),
+            gr.Radio(
+                [
+                "ChatGPT", #1
+                "Phi-4", #2
+                "Phi-3", #3
+                "Gemma-3", #4
+                "Gemini-2-Flash-Think", #6
+                "Gemini-Flash", #7
+                "QwQ-32B", #9
+                "Qwen2.5", #11
+                "DeepSeek-R1", #12
+                "DeepSeek-R1-Zero", #12
+                "Llama-3.3", #15
+                "Mistral-Small", #16
+                "Mistral-Nemo", #17
+                "Zephyr", #18
+                "Olympic-Coder", #20
+                "LearnLM", #8
+                "Reka-Flash", #21
+                "OpenChat", #22
+                "Toppy", #22
+                "MoonLight", #22
+                ],
+            label = "Choose AI Model", value = "ChatGPT"),
+            gr.Textbox(label = "Your Name", placeholder = "Enter Your Name"),
+            gr.Textbox(label = "Your Query", placeholder = "Enter Your Question"),
+            gr.Textbox(label = "System Prompt", placeholder = "Enter Custom System Propmt (Optional)"),
+        ],
+        outputs = [gr.Textbox(label ="AI Response", lines = 25)],
+        flagging_mode = "never"
+    ).launch(share=True)
+    # ).launch(share=False, server_port=54321)
+
+if __name__ == '__main__':
+    main()

models.py

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+from dotenv import load_dotenv
+import os
+import openai
+import pandas as pd
+
+load_dotenv()
+path = os.getenv("DATA")
+
+def add_new(model,name,query,prompt,answer):
+	data = {"name" : name, "model" : model, "query" : query, "prompt": prompt, "answer" : answer}
+	row = pd.DataFrame([data])
+	if not os.path.isfile(path):
+		os.makedirs("data")
+		with open(path, 'w') as file:
+			file.write("name,model,query,prompt,answer\n")
+		print("File Created")
+	old_df = pd.read_csv(path)
+	new_df = pd.concat([old_df, row], ignore_index=True)
+	new_df.to_csv(path, index=False)
+	print("Saved.")
+
+def get_data():
+    df = pd.read_csv(path)  
+    return df.to_string()
+
+def get_text():
+	with open(path, 'r', encoding='utf-8') as file:
+		text = file.read()
+	return text
+
+class ModelChain:
+	def __init__(self):
+		self.client = self.generate_client(os.getenv("OPENROUTER_API_KEY"), os.getenv("BASE_URL"))
+		self.deepseek_messages = []
+		self.gemini_messages = []
+
+	def generate_client(self,api_key, url):
+		return openai.OpenAI(
+			api_key = api_key,
+			base_url = url,
+		)
+
+	def generate_response(self,model,name, query,prompt):
+		messages = [{"role":"system","content": prompt},
+					{"role":"user","content":query}]
+		try:
+			result = self.client.chat.completions.create(
+				model = model,
+				messages = messages,
+			)
+			answer = result.choices[0].message.content
+			add_new(model,name,query,prompt,answer)
+			return answer
+		except Exception as e:
+			print(f"Response Error : {e}")
+			if e == "'NoneType' object is not subscriptable":
+				return f"This AI Model might be Busy at the moment, try another AI Model"
+			else:
+				return f"Response Error : {e}"
+
+def main():
+	chain= Modelschain()
+
+if __name__ == "__main__":
+	main()

requirements.txt

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+huggingface_hub==0.25.2
+gradio
+openai
+dotenv