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.gitignore

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+# Ignore virtual environment files
+venv/
+env/
+*.env
+# Ignore IDE files
+.vscode/
+.idea/
+.pycharm/

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from huggingface_hub import HfFolder
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Set token directly
+api_key = os.getenv('HF_API_KEY')
+
+model_name = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)
+model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=True)
+
+# Define chatbot function
+def chatbot(prompt):
+    system_prompt = (
+        "You are a helpful coding assistant. Answer questions with clear and concise explanations. "
+        "Provide examples using proper Markdown formatting for code blocks.\n\n"
+        "Question: {user_prompt}\n\nAnswer:"
+    )
+    final_prompt = system_prompt.format(user_prompt=prompt)
+    inputs = tokenizer(final_prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return f"**Here is the response:**\n\n{response.strip()}"
+
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=chatbot,
+    inputs="text",
+    outputs="text",
+    title="Coding Chatbot",
+    description="Ask coding questions and get AI-generated code!",
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()