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README.md

@@ -1,12 +1 @@
----
-title: Dyagnosysapi
-emoji: 🏆
-colorFrom: indigo
-colorTo: purple
-sdk: docker
-pinned: false
-license: apache-2.0
-short_description: API
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# dyagnosysAPI

main.py

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+# main.py
+
+from fastapi import FastAPI, File, UploadFile, HTTPException, Form
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+import librosa
+import numpy as np
+import tempfile
+import os
+import warnings
+import re
+import matplotlib.pyplot as plt
+
+warnings.filterwarnings("ignore", category=UserWarning, module='librosa')
+
+app = FastAPI()
+
+def extract_audio_features(audio_file_path):
+    # Load the audio file and extract features
+    y, sr = librosa.load(audio_file_path, sr=None)
+    f0, voiced_flag, voiced_probs = librosa.pyin(y, fmin=75, fmax=600)
+    f0 = f0[~np.isnan(f0)]
+    energy = librosa.feature.rms(y=y)[0]
+    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+    onset_env = librosa.onset.onset_strength(y=y, sr=sr)
+    tempo, _ = librosa.beat.beat_track(onset_envelope=onset_env, sr=sr)
+    speech_rate = tempo / 60
+    return f0, energy, speech_rate, mfccs, y, sr
+
+def analyze_voice_stress(audio_file_path):
+    f0, energy, speech_rate, mfccs, y, sr = extract_audio_features(audio_file_path)
+    mean_f0 = np.mean(f0)
+    std_f0 = np.std(f0)
+    mean_energy = np.mean(energy)
+    std_energy = np.std(energy)
+    gender = 'male' if mean_f0 < 165 else 'female'
+    norm_mean_f0 = 110 if gender == 'male' else 220
+    norm_std_f0 = 20
+    norm_mean_energy = 0.02
+    norm_std_energy = 0.005
+    norm_speech_rate = 4.4
+    norm_std_speech_rate = 0.5
+    z_f0 = (mean_f0 - norm_mean_f0) / norm_std_f0
+    z_energy = (mean_energy - norm_mean_energy) / norm_std_energy
+    z_speech_rate = (speech_rate - norm_speech_rate) / norm_std_speech_rate
+    stress_score = (0.4 * z_f0) + (0.4 * z_speech_rate) + (0.2 * z_energy)
+    stress_level = float(1 / (1 + np.exp(-stress_score)) * 100)
+    categories = ["Very Low Stress", "Low Stress", "Moderate Stress", "High Stress", "Very High Stress"]
+    category_idx = min(int(stress_level / 20), 4)
+    stress_category = categories[category_idx]
+    return {"stress_level": stress_level, "category": stress_category, "gender": gender}
+
+def analyze_text_stress(text: str):
+    # Placeholder text stress analysis
+    stress_keywords = ["anxious", "nervous", "stress", "panic", "tense"]
+    stress_score = sum([1 for word in stress_keywords if word in text.lower()])
+    
+    # Normalize the score for a basic assessment
+    stress_level = min(stress_score * 20, 100)
+    categories = ["Very Low Stress", "Low Stress", "Moderate Stress", "High Stress", "Very High Stress"]
+    category_idx = min(int(stress_level / 20), 4)
+    stress_category = categories[category_idx]
+    
+    return {"stress_level": stress_level, "category": stress_category}
+
+class StressResponse(BaseModel):
+    stress_level: float
+    category: str
+    gender: str = None  # Optional, only for audio analysis
+
+@app.post("/analyze-stress/", response_model=StressResponse)
+async def analyze_stress(
+    file: UploadFile = File(None), 
+    file_path: str = Form(None),
+    text: str = Form(None)
+):
+    if file is None and file_path is None and text is None:
+        raise HTTPException(status_code=400, detail="Either a file, file path, or text input is required.")
+    
+    # Handle audio file analysis
+    if file or file_path:
+        if file:
+            if not file.filename.endswith(".wav"):
+                raise HTTPException(status_code=400, detail="Only .wav files are supported.")
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
+                temp_file.write(await file.read())
+                temp_file_path = temp_file.name
+        else:
+            if not file_path.endswith(".wav"):
+                raise HTTPException(status_code=400, detail="Only .wav files are supported.")
+            if not os.path.exists(file_path):
+                raise HTTPException(status_code=400, detail="File path does not exist.")
+            temp_file_path = file_path
+
+        try:
+            result = analyze_voice_stress(temp_file_path)
+            return JSONResponse(content=result)
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+        finally:
+            if file:
+                os.remove(temp_file_path)
+
+    # Handle text analysis
+    elif text:
+        result = analyze_text_stress(text)
+        return JSONResponse(content=result)
+
+if __name__ == "__main__":
+    import uvicorn
+    port = int(os.getenv("PORT", 8000))  # Use the PORT environment variable for Render compatibility
+    uvicorn.run("main:app", host="0.0.0.0", port=port, reload=True)

requirements.txt

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+fastapi
+uvicorn
+pydantic
+librosa
+numpy
+matplotlib
+python-multipart