Update app.py

app.py

@@ -1,20 +1,22 @@
 import streamlit as st
 import requests
-import os  # Import os to access environment variables
+import os  # To access environment variables
+import google.generativeai as genai  # Import Gemini API
 
-# Get API token from environment variable
-API_TOKEN = os.getenv("HF_API_TOKEN")
+# Load API keys from environment variables
+HF_API_TOKEN = os.getenv("HF_API_TOKEN")
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
 
-
-# Change MODEL_ID to a better model
+# Set up Hugging Face API
 MODEL_ID = "Salesforce/codet5p-770m"  # CodeT5+ (Recommended)
-# MODEL_ID = "bigcode/starcoder2-15b"  # StarCoder2
-# MODEL_ID = "bigcode/starcoder"
 API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}"
-HEADERS = {"Authorization": f"Bearer {API_TOKEN}"}
+HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+
+# Initialize Gemini API
+genai.configure(api_key=GEMINI_API_KEY)
 
 def translate_code(code_snippet, source_lang, target_lang):
-    """Translate code using Hugging Face API securely."""
+    """Translate code using Hugging Face API."""
     prompt = f"Translate the following {source_lang} code to {target_lang}:\n\n{code_snippet}\n\nTranslated {target_lang} Code:\n"
 
     response = requests.post(API_URL, headers=HEADERS, json={
@@ -23,7 +25,6 @@ def translate_code(code_snippet, source_lang, target_lang):
             "max_new_tokens": 150,
             "temperature": 0.2,
             "top_k": 50
-            # "stop": ["\n\n", "#", "//", "'''"]
         }
     })
 
@@ -34,8 +35,23 @@ def translate_code(code_snippet, source_lang, target_lang):
     else:
         return f"Error: {response.status_code}, {response.text}"
 
+def fallback_translate_with_gemini(code_snippet, source_lang, target_lang):
+    """Fallback function using Gemini API for translation."""
+    prompt = f"""You are a code translation expert. Convert the following {source_lang} code to {target_lang}:
+    
+    {code_snippet}
+
+    Ensure the translation is accurate and follows {target_lang} best practices.
+    """
+    try:
+        model = genai.GenerativeModel("gemini-pro")
+        response = model.generate_content(prompt)
+        return response.text.strip() if response else "Translation failed."
+    except Exception as e:
+        return f"Gemini API Error: {str(e)}"
+
 # Streamlit UI
-st.title("🔄 Code Translator using StarCoder")
+st.title("🔄 Code Translator with Gemini AI")
 st.write("Translate code between different programming languages using AI.")
 
 languages = ["Python", "Java", "C++", "C"]
@@ -44,11 +60,96 @@ source_lang = st.selectbox("Select source language", languages)
 target_lang = st.selectbox("Select target language", languages)
 code_input = st.text_area("Enter your code here:", height=200)
 
+# Initialize session state
+if "translate_attempts" not in st.session_state:
+    st.session_state.translate_attempts = 0
+    st.session_state.translated_code = ""
+
 if st.button("Translate"):
     if code_input.strip():
+        st.session_state.translate_attempts += 1
         with st.spinner("Translating..."):
-            translated_code = translate_code(code_input, source_lang, target_lang)
-            st.subheader("Translated Code:")
-            st.code(translated_code, language=target_lang.lower())
+            if st.session_state.translate_attempts == 1:
+                # First attempt using the pretrained model
+                st.session_state.translated_code = translate_code(code_input, source_lang, target_lang)
+            else:
+                # Second attempt uses Gemini API
+                st.session_state.translated_code = fallback_translate_with_gemini(code_input, source_lang, target_lang)
+
+        st.subheader("Translated Code:")
+        st.code(st.session_state.translated_code, language=target_lang.lower())
     else:
         st.warning("⚠️ Please enter some code before translating.")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# V1 without gemini api
+
+# import streamlit as st
+# import requests
+# import os  # Import os to access environment variables
+
+# # Get API token from environment variable
+# API_TOKEN = os.getenv("HF_API_TOKEN")
+
+
+# # Change MODEL_ID to a better model
+# MODEL_ID = "Salesforce/codet5p-770m"  # CodeT5+ (Recommended)
+# # MODEL_ID = "bigcode/starcoder2-15b"  # StarCoder2
+# # MODEL_ID = "bigcode/starcoder"
+# API_URL = f"https://api-inference.huggingface.co/models/{MODEL_ID}"
+# HEADERS = {"Authorization": f"Bearer {API_TOKEN}"}
+
+# def translate_code(code_snippet, source_lang, target_lang):
+#     """Translate code using Hugging Face API securely."""
+#     prompt = f"Translate the following {source_lang} code to {target_lang}:\n\n{code_snippet}\n\nTranslated {target_lang} Code:\n"
+
+#     response = requests.post(API_URL, headers=HEADERS, json={
+#         "inputs": prompt,
+#         "parameters": {
+#             "max_new_tokens": 150,
+#             "temperature": 0.2,
+#             "top_k": 50
+#             # "stop": ["\n\n", "#", "//", "'''"]
+#         }
+#     })
+
+#     if response.status_code == 200:
+#         generated_text = response.json()[0]["generated_text"]
+#         translated_code = generated_text.split(f"Translated {target_lang} Code:\n")[-1].strip()
+#         return translated_code
+#     else:
+#         return f"Error: {response.status_code}, {response.text}"
+
+# # Streamlit UI
+# st.title("🔄 Code Translator using StarCoder")
+# st.write("Translate code between different programming languages using AI.")
+
+# languages = ["Python", "Java", "C++", "C"]
+
+# source_lang = st.selectbox("Select source language", languages)
+# target_lang = st.selectbox("Select target language", languages)
+# code_input = st.text_area("Enter your code here:", height=200)
+
+# if st.button("Translate"):
+#     if code_input.strip():
+#         with st.spinner("Translating..."):
+#             translated_code = translate_code(code_input, source_lang, target_lang)
+#             st.subheader("Translated Code:")
+#             st.code(translated_code, language=target_lang.lower())
+#     else:
+#         st.warning("⚠️ Please enter some code before translating.")