Update app.py

app.py

@@ -1,64 +1,55 @@
 import os
 import time
-import json
-import httpx
 import gradio as gr
 from huggingface_hub import InferenceClient
 from dotenv import load_dotenv
 
 # Load API keys from .env file
 load_dotenv()
-HF_API_KEY = os.getenv("HF_API_KEY")  # Hugging Face API for Gemma & Llama
-TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY")  # Together AI API for DeepSeek-V3
+HF_API_KEY = os.getenv("HF_API_KEY")  # Hugging Face API Key
+TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY")  # Together AI API Key
 
-# Initialize Hugging Face Clients
-hf_client = InferenceClient(provider="hf-inference", api_key=HF_API_KEY)
+# Initialize clients
+gemma_client = InferenceClient(provider="hf-inference", api_key=HF_API_KEY)
 together_client = InferenceClient(provider="together", api_key=TOGETHER_API_KEY)
 
-# Query Hugging Face Models (Gemma & Llama)
-def query_huggingface_model(user_input, model_name):
-    try:
-        messages = [{"role": "user", "content": user_input}]
-        completion = hf_client.chat.completions.create(
-            model=model_name, 
-            messages=messages, 
-            max_tokens=500
-        )
-        return completion.choices[0].message["content"]
-    except Exception as e:
-        return f"Error querying {model_name}: {str(e)}"
-
-# Query DeepSeek-V3 (Together AI via Hugging Face)
-def query_deepseek_v3(user_input):
-    try:
-        messages = [{"role": "user", "content": user_input}]
-        completion = together_client.chat.completions.create(
-            model="deepseek-ai/DeepSeek-V3", 
-            messages=messages, 
-            max_tokens=500
-        )
-        return completion.choices[0].message["content"]
-    except Exception as e:
-        return f"Error querying DeepSeek-V3: {str(e)}"
+# Function to query Hugging Face (Gemma)
+def query_gemma(user_input):
+    messages = [{"role": "user", "content": user_input}]
+    completion = gemma_client.chat.completions.create(
+        model="google/gemma-2-27b-it",
+        messages=messages,
+        max_tokens=500
+    )
+    return completion.choices[0].message["content"]
+
+# Function to query Together (Llama)
+def query_llama(user_input):
+    messages = [{"role": "user", "content": user_input}]
+    completion = together_client.chat.completions.create(
+        model="meta-llama/Llama-3.3-70B-Instruct",
+        messages=messages,
+        max_tokens=500
+    )
+    return completion.choices[0].message["content"]
+
+# Function to query Together (DeepSeek)
+def query_deepseek(user_input):
+    messages = [{"role": "user", "content": user_input}]
+    completion = together_client.chat.completions.create(
+        model="deepseek-ai/DeepSeek-V3",
+        messages=messages,
+        max_tokens=500
+    )
+    return completion.choices[0].message["content"]
 
 # Function to refine responses using DeepSeek-V3
 def refine_response(user_input):
     try:
-        # Get responses from all three models
-        gemma_response = query_huggingface_model(user_input, "google/gemma-2-27b-it")
-        llama_response = query_huggingface_model(user_input, "meta-llama/Llama-3.3-70B-Instruct")
-        deepseek_response = query_deepseek_v3(user_input)
-
-        # If any response is missing, return the available ones
-        responses = {
-            "Gemma": gemma_response.strip(),
-            "Llama": llama_response.strip(),
-            "DeepSeek-V3": deepseek_response.strip()
-        }
-        valid_responses = {k: v for k, v in responses.items() if v}
-
-        if len(valid_responses) < 2:
-            return "\n\n".join(f"{k} Response: {v}" for k, v in valid_responses.items())
+        # Get responses
+        gemma_response = query_gemma(user_input)
+        llama_response = query_llama(user_input)
+        deepseek_response = query_deepseek(user_input)
 
         # Prepare refinement prompt
         improvement_prompt = f"""
@@ -68,33 +59,17 @@ def refine_response(user_input):
         Response 2 (Llama 3.3): {llama_response}
         Response 3 (DeepSeek-V3): {deepseek_response}
 
-        Please combine the best elements of all three, improve clarity, and provide a final refined answer.
+        Please combine the best elements of all three and provide an improved answer.
         """
 
-        # Retry loop for DeepSeek-V3 refinement
-        max_retries = 3
-        for attempt in range(max_retries):
-            try:
-                messages = [{"role": "user", "content": improvement_prompt}]
-                refined_completion = together_client.chat.completions.create(
-                    model="deepseek-ai/DeepSeek-V3",
-                    messages=messages,
-                    max_tokens=500
-                )
-
-                refined_content = refined_completion.choices[0].message["content"]
-
-                if refined_content.strip():
-                    return refined_content
-                else:
-                    print("Received empty response from DeepSeek-V3, retrying...")
-                    time.sleep(2)
-
-            except Exception as e:
-                print(f"Error on attempt {attempt + 1}: {str(e)}")
-                time.sleep(2)
+        messages = [{"role": "user", "content": improvement_prompt}]
+        refined_completion = together_client.chat.completions.create(
+            model="deepseek-ai/DeepSeek-V3",
+            messages=messages,
+            max_tokens=500
+        )
 
-        return f"Refinement failed. Here’s the best available response:\n\n{max(valid_responses.values(), key=len)}"
+        return refined_completion.choices[0].message["content"]
 
     except Exception as e:
         return f"Error refining response: {str(e)}"
@@ -105,8 +80,8 @@ iface = gr.Interface(
     inputs=gr.Textbox(lines=2, placeholder="Ask me anything..."), 
     outputs="text",
     title="Multi-Model AI Enhancer",
-    description="Get responses from Gemma, Llama 3.3, and DeepSeek-V3. Then receive an improved final answer."
+    description="Get responses from Gemma, Llama, and DeepSeek. Then receive an improved answer."
 )
 
 # Launch app
-iface.launch(debug=True)
+iface.launch()