Update app.py

app.py

@@ -1,4 +1,3 @@
-import os
 import requests
 import streamlit as st
 from PIL import Image
@@ -23,41 +22,48 @@ groq_headers = {
 
 # Function to query Hugging Face model for image generation
 def query_huggingface(payload):
-    response = requests.post(HF_API_URL, headers=hf_headers, json=payload)
-    if response.status_code != 200:
-        st.error(f"Error: {response.status_code} - {response.text}")
+    try:
+        response = requests.post(HF_API_URL, headers=hf_headers, json=payload)
+        if response.status_code != 200:
+            return None
+        return response.content
+    except Exception:
         return None
-    return response.content
 
 # Function to generate text using Groq API
 def generate_response(prompt):
-    payload = {
-        "model": "mixtral-8x7b-32768",
-        "messages": [
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": prompt}
-        ],
-        "max_tokens": 100,
-        "temperature": 0.7
-    }
-    response = requests.post(groq_url, json=payload, headers=groq_headers)
-    if response.status_code == 200:
-        result = response.json()
-        return result['choices'][0]['message']['content']
-    else:
-        st.error(f"Error: {response.status_code} - {response.text}")
+    try:
+        payload = {
+            "model": "mixtral-8x7b-32768",
+            "messages": [
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": prompt}
+            ],
+            "max_tokens": 100,
+            "temperature": 0.7
+        }
+        response = requests.post(groq_url, json=payload, headers=groq_headers)
+        if response.status_code == 200:
+            result = response.json()
+            return result['choices'][0]['message']['content']
+        else:
+            return None
+    except Exception:
         return None
 
 # Function to translate Tamil to English using MBart model
 def translate_tamil_to_english(tamil_text):
-    model_name = "facebook/mbart-large-50-many-to-one-mmt"
-    model = MBartForConditionalGeneration.from_pretrained(model_name)
-    tokenizer = MBart50Tokenizer.from_pretrained(model_name, src_lang="ta_IN")
-
-    inputs = tokenizer(tamil_text, return_tensors="pt")
-    translated = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
-    translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
-    return translated_text
+    try:
+        model_name = "facebook/mbart-large-50-many-to-one-mmt"
+        model = MBartForConditionalGeneration.from_pretrained(model_name)
+        tokenizer = MBart50Tokenizer.from_pretrained(model_name, src_lang="ta_IN")
+
+        inputs = tokenizer(tamil_text, return_tensors="pt")
+        translated = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
+        translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
+        return translated_text
+    except Exception:
+        return None
 
 # Main function to generate text and image
 def generate_image_and_text(user_input):
@@ -67,40 +73,36 @@ def generate_image_and_text(user_input):
         # Translate Tamil to English
         english_input = translate_tamil_to_english(user_input)
         if not english_input:
-            st.error("Error in translation.")
-            return
-
-        # Generate text description (100 tokens) and image prompt (30 tokens) using Groq API
-        full_text_description = generate_response(english_input)
-        if not full_text_description:
-            st.error("Error in text generation.")
-            return
-
-        # Create image prompt based on the full text description
-        image_prompt = generate_response(f"Create a concise image prompt from the following text: {full_text_description}")
-        if not image_prompt:
-            st.error("Error in generating image prompt.")
-            return
-
-        # Request an image based on the generated image prompt
-        image_data = query_huggingface({"inputs": image_prompt})
-        if not image_data:
-            st.error("Error in image generation.")
-            return
-
-        # Display the results
-        st.markdown("### Translated English Text:")
-        st.write(english_input)
-
-        st.markdown("### Generated Text Response:")
-        st.write(full_text_description)
-
-        try:
-            # Load and display the image
-            image = Image.open(BytesIO(image_data))
-            st.image(image, caption="Generated Image", use_column_width=True)
-        except Exception as e:
-            st.error(f"Failed to display image: {e}")
+            st.warning("Sorry, the translation model is unavailable right now 😥😥😥. Please try again later.")
+        else:
+            st.markdown("### Translated English Text:")
+            st.write(english_input)
+
+        # Generate text description (100 tokens) using Groq API
+        if english_input:
+            full_text_description = generate_response(english_input)
+            if not full_text_description:
+                st.warning("Sorry, the text generation model is unavailable right now 😥😥😥. Please try again later.")
+            else:
+                st.markdown("### Generated Text Response:")
+                st.write(full_text_description)
+
+                # Create image prompt based on the full text description
+                image_prompt = generate_response(f"Create a concise image prompt from the following text: {full_text_description}")
+                if not image_prompt:
+                    st.warning("Sorry, the image prompt model is unavailable right now 😥😥😥. Please try again later.")
+                else:
+                    # Request an image based on the generated image prompt
+                    image_data = query_huggingface({"inputs": image_prompt})
+                    if not image_data:
+                        st.warning("Sorry, the image generation model is unavailable right now 😥😥😥. Please try again later.")
+                    else:
+                        try:
+                            # Load and display the image
+                            image = Image.open(BytesIO(image_data))
+                            st.image(image, caption="Generated Image", use_column_width=True)
+                        except Exception as e:
+                            st.error(f"Failed to display image: {e}")
 
 # Streamlit interface
 st.title("Multi-Modal Generator (Tamil to English)")