Create app.py

app.py

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+import os
+import streamlit as st
+import torch
+import tempfile
+from groq import Groq
+from diffusers import AutoPipelineForText2Image
+from io import BytesIO
+
+# Load API keys
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+HF_API_KEY = os.getenv("HF_API_KEY")
+
+# Initialize Groq client
+client = Groq(api_key=GROQ_API_KEY)
+
+# Load image generation model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+image_gen = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", use_auth_token=HF_API_KEY).to(device)
+
+# Function to transcribe Tamil audio using Groq's Whisper
+def transcribe(audio_bytes):
+    if not audio_bytes:
+        return "No audio provided."
+
+    # Save the audio file temporarily
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
+        temp_audio.write(audio_bytes)
+        temp_audio_path = temp_audio.name
+
+    # Call Whisper API
+    with open(temp_audio_path, "rb") as file:
+        transcription = client.audio.transcriptions.create(
+            file=file,
+            model="whisper-large-v3",
+            language="ta",
+            response_format="verbose_json"
+        )
+
+    # Cleanup temp file
+    os.remove(temp_audio_path)
+    
+    return transcription["text"]
+
+# Function to translate Tamil to English using Groq's Gemma
+def translate_text(tamil_text):
+    response = client.chat.completions.create(
+        model="gemma-7b-it",
+        messages=[{"role": "user", "content": f"Translate this Tamil text to English: {tamil_text}"}]
+    )
+    return response.choices[0].message.content
+
+# Function to generate text using Groq's DeepSeek R1
+def generate_text(prompt):
+    response = client.chat.completions.create(
+        model="deepseek-coder-r1-7b",
+        messages=[{"role": "user", "content": f"Write a short story about: {prompt}"}]
+    )
+    return response.choices[0].message.content
+
+# Function to generate an image
+def generate_image(prompt):
+    img = image_gen(prompt=prompt).images[0]
+    return img
+
+# Streamlit UI
+st.title("🎤 Tamil Speech to Image & Story Generator")
+
+# Upload audio file
+audio_file = st.file_uploader("Upload a Tamil audio file", type=["wav", "mp3"])
+
+if st.button("Generate"):
+    if audio_file is not None:
+        # Read audio bytes
+        audio_bytes = audio_file.read()
+
+        # Process Steps
+        tamil_text = transcribe(audio_bytes)
+        english_text = translate_text(tamil_text)
+        story = generate_text(english_text)
+        image = generate_image(english_text)
+
+        # Display Outputs
+        st.subheader("📝 Transcribed Tamil Text")
+        st.write(tamil_text)
+
+        st.subheader("🔠 Translated English Text")
+        st.write(english_text)
+
+        st.subheader("📖 Generated Story")
+        st.write(story)
+
+        st.subheader("🖼️ Generated Image")
+        st.image(image, caption="Generated Image from Story")
+
+    else:
+        st.warning("⚠️ Please upload an audio file before generating.")
+