Create app.py

app.py

@@ -0,0 +1,200 @@
+import re
+import gradio as gr
+import time
+from typing import List, Dict
+
+# Mock backend responses
+class MockBackend:
+    def generate_response(self, prompt: str, model: str) -> str:
+        # Simulate some processing time
+        time.sleep(1)
+        
+        # Mock responses for different types of queries
+        if "binary search" in prompt.lower():
+            return """
+def binary_search(arr: list, target: int) -> int:
+    '''
+    Implements binary search algorithm
+    Args:
+        arr: Sorted list of numbers
+        target: Number to find
+    Returns:
+        Index of target if found, -1 otherwise
+    '''
+    left, right = 0, len(arr) - 1
+    
+    while left <= right:
+        mid = (left + right) // 2
+        if arr[mid] == target:
+            return mid
+        elif arr[mid] < target:
+            left = mid + 1
+        else:
+            right = mid - 1
+    
+    return -1
+
+# Example usage:
+numbers = [1, 3, 5, 7, 9, 11, 13, 15]
+result = binary_search(numbers, 7)
+print(f"Found 7 at index: {result}")  # Output: Found 7 at index: 3
+"""
+        elif "machine learning" in prompt.lower():
+            return "Machine Learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. It uses statistical techniques to allow computers to 'learn' from data."
+        else:
+            return f"Mock response for query: {prompt}"
+
+# Initialize mock backend and query list
+mock_backend = MockBackend()
+queries_list = []
+
+# Model name mappings
+llm_name2id = {
+    "Llama-3.1-70B-Versatile": "llama-3.1-70b-versatile",
+    "Llama-3-70B-8192": "llama3-70b-8192",
+    "Llama-3-8B-8192": "llama3-8b-8192"
+}
+
+# Default values
+DEFAULT_TEMP = 0.2
+DEFAULT_MODEL = "Llama-3-70B-8192"
+DEFAULT_USE_RAG = True
+
+def clear_queries():
+    global queries_list
+    queries_list = []
+    return "", DEFAULT_MODEL, DEFAULT_TEMP, DEFAULT_USE_RAG, "", ""
+
+def add_to_list(query_txt: str, model: str, temperature: float, use_rag: bool) -> tuple:
+    global queries_list
+    
+    if len(query_txt.strip()) > 0:
+        queries_list.append({
+            "prompt": query_txt.strip(),
+            "temperature": str(temperature),
+            "model": llm_name2id[model],
+            "use_rag": str(use_rag),
+        })
+    
+    return "", generate_queries_str(queries_list)
+
+def submit(query_txt: str, model: str, temperature: float, use_rag: bool) -> tuple:
+    global queries_list
+    
+    if len(query_txt.strip()) > 0:
+        _, queries = add_to_list(query_txt, model, temperature, use_rag)
+    else:
+        queries = generate_queries_str(queries_list)
+    
+    if len(queries_list) > 0:
+        # Use mock backend instead of HTTP requests
+        answers = []
+        for query in queries_list:
+            response = mock_backend.generate_response(query["prompt"], query["model"])
+            answers.append({"answer": response})
+        
+        answers_str = generate_answers_str(answers)
+        queries_list = []
+    else:
+        answers_str = "No queries submitted yet."
+    
+    return "", queries, answers_str
+
+def generate_queries_str(queries: List[Dict]) -> str:
+    delimiter = f"\n{'-' * 120}\n"
+    return delimiter.join([f"Query: {q['prompt']}" for q in queries])
+
+def generate_answers_str(answers: List[Dict]) -> str:
+    delimiter = f"\n{'-' * 120}\n"
+    return delimiter.join([clean(a["answer"]) for a in answers])
+
+def clean(answer_str: str) -> str:
+    answer_str = re.sub('^\s*:', '', answer_str)
+    
+    garbages = [
+        "Here is the generated paragraph:",
+        "Let me know if this meets your requirements!",
+    ]
+    for g in garbages:
+        answer_str = answer_str.replace(g, "")
+    return answer_str.strip()
+
+if __name__ == "__main__":
+    with gr.Blocks(theme=gr.themes.Default()) as demo:
+        gr.Markdown("""
+        # Multilingual LLM Interface (Mock Version)
+        Enter your query in any language. This is a mock version for testing - responses are simulated.
+        
+        Test queries:
+        1. Ask about "binary search" to get a code implementation
+        2. Ask about "machine learning" to get an explanation
+        3. Try any other query to see mock responses
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=2):
+                query_txt = gr.Textbox(
+                    placeholder="Enter your query here...",
+                    label="Query",
+                    lines=3
+                )
+            with gr.Column(scale=1):
+                model = gr.Radio(
+                    choices=[
+                        "Llama-3-8B-8192",
+                        "Llama-3-70B-8192",
+                        "Llama-3.1-70B-Versatile",
+                    ],
+                    value=DEFAULT_MODEL,
+                    label="Select Model"
+                )
+                use_rag = gr.Checkbox(
+                    value=DEFAULT_USE_RAG,
+                    label="Enable RAG (Retrieval-Augmented Generation)"
+                )
+                temperature = gr.Slider(
+                    minimum=0,
+                    maximum=1,
+                    value=DEFAULT_TEMP,
+                    step=0.1,
+                    label="Temperature (Creativity)"
+                )
+        
+        with gr.Row():
+            clear_btn = gr.Button("Clear All", variant="stop")
+            add_btn = gr.Button("Add Query", variant="secondary")
+            submit_btn = gr.Button("Submit All", variant="primary")
+        
+        with gr.Row():
+            with gr.Column():
+                queries_box = gr.Textbox(
+                    placeholder="Your queries will appear here...",
+                    label="Submitted Queries",
+                    interactive=False,
+                    lines=5
+                )
+            with gr.Column():
+                answers_box = gr.Textbox(
+                    placeholder="Model responses will appear here...",
+                    label="Model Responses",
+                    interactive=False,
+                    lines=5
+                )
+        
+        clear_btn.click(
+            fn=clear_queries,
+            inputs=[],
+            outputs=[query_txt, model, temperature, use_rag, queries_box, answers_box]
+        )
+        add_btn.click(
+            fn=add_to_list,
+            inputs=[query_txt, model, temperature, use_rag],
+            outputs=[query_txt, queries_box]
+        )
+        submit_btn.click(
+            fn=submit,
+            inputs=[query_txt, model, temperature, use_rag],
+            outputs=[query_txt, queries_box, answers_box]
+        )
+
+    demo.launch()