Create app.py

app.py

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+import gradio as gr
+import os
+import uuid
+from gtts import gTTS
+import whisper
+import openai
+
+# Load models once
+model = whisper.load_model("base")
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# LLM prompt template
+PROMPT_TEMPLATE = """
+You're a communication coach helping users improve their spoken English.
+
+Evaluate the following speech transcript and provide helpful, constructive feedback.
+
+Focus on:
+- Clarity
+- Fluency
+- Grammar
+- Filler words
+- Tone and pacing
+- Sentence structure
+- Overall communication effectiveness
+
+Transcript:
+\"\"\"{transcript}\"\"\"
+
+Provide concise feedback in 3-4 bullet points followed by a one-line motivational sentence.
+"""
+
+def generate_feedback_with_llm(transcript):
+    prompt = PROMPT_TEMPLATE.format(transcript=transcript.strip())
+
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        temperature=0.7,
+        messages=[
+            {"role": "system", "content": "You are a helpful communication tutor."},
+            {"role": "user", "content": prompt}
+        ]
+    )
+
+    return response.choices[0].message["content"]
+
+def tutor_feedback(audio_file):
+    # Step 1: Transcribe
+    result = model.transcribe(audio_file)
+    transcript = result["text"]
+
+    # Step 2: Analyze via LLM
+    feedback_text = generate_feedback_with_llm(transcript)
+
+    # Step 3: TTS
+    tts = gTTS(feedback_text)
+    output_path = f"/tmp/{uuid.uuid4()}.mp3"
+    tts.save(output_path)
+
+    return transcript, feedback_text, output_path
+
+# Gradio UI
+iface = gr.Interface(
+    fn=tutor_feedback,
+    inputs=gr.Audio(source="microphone", type="filepath", label="🎤 Speak your response"),
+    outputs=[
+        gr.Textbox(label="📝 Transcript"),
+        gr.Textbox(label="📢 Tutor Feedback"),
+        gr.Audio(label="🔊 Spoken Feedback", type="filepath")
+    ],
+    title="🗣 Communications Tutor with LLM Feedback",
+    description="Speak your answer. The AI tutor will transcribe it, analyze it using GPT, and give spoken feedback on how to improve."
+)
+
+iface.launch()